Making the Most of Mealtimes (M3): Determinants of Food Intake in Long Term Care
Conditions: Malnutrition
Sponsor: University of Waterloo

STUDY PROTOCOL
Open Access
Making the Most of Mealtimes (M3):
protocol of a multi-centre cross-sectional
study of food intake and its determinants
in older adults living in long term care
homes
Heather H. Keller1*, Natalie Carrier2, Susan Slaughter3, Christina Lengyel4, Catriona M. Steele5,6, Lisa Duizer7,
K. Steve Brown8, Habib Chaudhury9, Minn N. Yoon10, Alison M. Duncan11, Veronique M. Boscart12,1,5,
George Heckman1 and Lita Villalon2
Abstract
Background: Older adults living in long term care (LTC) homes are nutritionally vulnerable, often consuming
insufficient energy, macro- and micronutrients to sustain their health and function. Multiple factors are proposed to
influence food intake, yet our understanding of these diverse factors and their interactions are limited. The purpose
of this paper is to fully describe the protocol used to examine determinants of food and fluid intake among older
adults participating in the Making the Most of Mealtimes (M3) study.
Methods: A conceptual framework that considers multi-level influences on mealtime experience, meal quality and
meal access was used to design this multi-site cross-sectional study. Data were collected from 639 participants
residing in 32 LTC homes in four Canadian provinces by trained researchers. Food intake was assessed with
three-days of weighed food intake (main plate items), as well as estimations of side dishes, beverages and snacks
and compared to the Dietary Reference Intake. Resident-level measures included: nutritional status, nutritional risk;
disease conditions, medication, and diet prescriptions; oral health exam, signs of swallowing difficulty and olfactory
ability; observed eating behaviours, type and number of staff assisting with eating; and food and foodservice
satisfaction. Function, cognition, depression and pain were assessed using interRAI LTCF with selected items
completed by researchers with care staff. Care staff completed a standardized person-directed care questionnaire.
Researchers assessed dining rooms for physical and psychosocial aspects that could influence food intake.
Management from each site completed a questionnaire that described the home, menu development, food
production, out-sourcing of food, staffing levels, and staff training. Hierarchical regression models, accounting for
clustering within province, home and dining room will be used to determine factors independently associated with
energy and protein intake, as proxies for intake. Proportions of residents at risk of inadequate diets will also be
determined.
(Continued on next page)
* Correspondence: hkeller@uwaterloo.ca
1Schlegel-University of Waterloo Research Institute for Aging, 250
Laurelwood Drive, Waterloo ON N2J 0E2, Canada
Full list of author information is available at the end of the article
© The Author(s). 2017 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Keller et al. BMC Geriatrics  (2017) 17:15 
DOI 10.1186/s12877-016-0401-4

(Continued from previous page)
Discussion: This rigorous and comprehensive data collection in a large and diverse sample will provide, for the first
time, the opportunity to consider important modifiable factors associated with poor food intake of residents in LTC.
Identification of factors that are independently associated with food intake will help to develop effective
interventions that support food intake.
Trial Registration: ClinicalTrials.gov ID: NCT02800291, retrospectively registered June 7, 2016.
Keywords: Food intake, Determinants, Dietary reference intake, Long term care homes
Background
Long term care (LTC), also referred to as care or nursing
homes, is a residential option that provides for the
instrumental and basic activities of daily living of their
clients. Older adults > 65 years of age) are the typical
resident and dementia is a common condition that is
managed in this setting. Poor food and fluid intake is the
primary cause for long term care (LTC) malnutrition [1]
resulting in falls, poor function, depression, mortality,
poor wound care and decreased quality of life for resi-
dents [1–7]. Resident energy intake has been reported to
be 1500 kcal/day or less [1, 8, 9] and up to 70% have
lower than recommended intakes of protein, fibre, cal-
cium, magnesium, zinc, and vitamins E, C, B6, thiamine,
riboflavin, niacin and/or folate [1, 8, 9]. Improving food
intake for residents in LTC has been identified as a pri-
ority by researchers, decision makers and clients (i.e.,
residents, families) to promote health and quality of life
[10]. Yet, rigorous collection of dietary intake data across
a diverse and large sample to truly understand preva-
lence of poor food intake in LTC is limited. For example,
some researchers have used a single day of recalled (by
resident or care staff) food intake to represent consump-
tion patterns, resulting in potentially flawed conclusions
on the adequacy of food intake [11–13]. Smaller studies
with weighed food records, the gold standard, have dem-
onstrated large inter-individual differences [12], but are
commonly limited in measurement of covariates and the
ability to model these due to the small sample size [12,
14]. Additionally, detailed descriptions of the food intake
profile are often missing [15]. Understanding the extent
of poor food intake and which nutrients are poorly con-
sumed, is relevant in identifying strategies to support
intake for residents in LTC. For example, if vitamin D is
low, supplementation may be the best strategy, whereas
if energy and a wide variety of micronutrients are low,
interventions that increase food intake, such as quality
eating assistance, may be required.
LTC malnutrition is both preventable and treatable;
[1, 16] successful interventions can improve the health
and function of residents [9, 17–19]. Innovative, multi-
level (i.e., targeting residents, staff, dining environment)
interventions are needed to address the problem of
inadequate food intake and consequent malnutrition in
LTC, as causes are likely to interact [20]. To develop inter-
ventions, a good understanding of the problem and its de-
terminants is needed. At this point, we have a limited
understanding of the problem of inadequate food intake
in LTC worldwide, and analyses have often been focused
on determinants that cannot be changed [21], such as de-
mentia. However, persons with dementia often require
some level of eating assistance [21] and this determinant
can be modified; staff can be trained on eating assisting
techniques [22], and sufficient time and accommodations
can be provided to promote eating independently (e.g.,
finger foods) [23]. Thus investigations focused on describ-
ing and determining the relative importance of these
amenable factors is needed to make advancements in
improving food intake and thus malnutrition in LTC.
Research to date has failed to use a comprehensive
conceptual framework to understand and intervene on
the multi-level and inter-related determinants of food in-
take in LTC residents [7, 15, 20, 24]. The proposed study
is built upon the M3 concept [25], which has its origins
in the Social Ecological Model [26], the Five Aspects of
Meal Model [27], and the Mealtimes as ‘Active Processes’
substantive theory [28]. The M3 conceptual model sug-
gests that multi-level determinants influence food and
fluid intake of residents. Specifically, it is hypothesized
that regional government regulations and standards,
LTC home policies and features, staffing levels, know-
ledge and practices, and resident characteristics will in-
fluence food intake. Within each of these levels, factors
in three domains of Meal Quality (nutritious, appealing
food); Meal Access (oral health, swallowing problems,
eating ability); and Mealtime Experience (dining environ-
ment) are relevant to food intake. This model drives the
design, data collection, analysis and interpretation of
results in this project [25].
Meal quality is operationally defined as food and fluid
offerings that are preferred and culturally appropriate,
appealing
(smell,
taste,
appearance),
served
at
an
acceptable temperature, and meeting the nutritional
requirements (i.e., Dietary Reference Intake [DRI]) of
residents. Quality food is favoured over oral nutritional
supplements or meal replacements by residents and
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 2 of 15

family for meeting nutritional needs and enhancing
quality of life [29]. Government policies and home-level
practices with respect to menu planning, choice of com-
mercial or in-house food products, food variety, food
budget, and the mandated role and time allocated to
clinical dietitians and other allied health professionals
(e.g.,
occupational
therapists
and
speech-language
pathologists) all have the potential to influence the types
and quality of food provided in LTC, yet we know little
about these determinants. Evidence to date suggests that
increased funding for food improves energy intake in
LTC residents [15]. A shorter menu cycle length is also
associated with increased malnutrition [30]. It has been
noted that LTC menus have been found to be low in
protein and micronutrients [1, 31] and are likely insuffi-
cient to support adequate health and function [32]. Of
specific concern are modified-texture foods (e.g., pureed)
that are frequently provided to persons with dysphagia
(i.e., swallowing impairment) to manage their swallowing
difficulty. These foods often have poor sensory appeal
and low nutrient density [33, 34].
Meal access is operationalized as those factors that in-
fluence food/fluid access and specifically: the availability
of food (meal timing, between meal snacks); the ability
to taste and smell; dysphagia; dentition and oral health
status; capacity with self feeding, time it takes to eat, and
any eating challenges; and the time provided for and
quality of eating assistance. Taste and smell are com-
monly impaired in older adults and specifically in those
with dementia [35, 36], potentially impairing food intake
and nutritional status [36]. Dysphagia is a significant
comorbidity that influences food intake and malnutrition
[21], although prevalence in LTC is elusive. Additionally,
dental factors such as loose teeth, poorly fitting den-
tures, and/or poor oral health may contribute to limited
food intake, preference for foods low in micronutrients
but easier to eat (e.g., ice cream, commercial puddings,
mashed potatoes) or prescription of modified-texture
foods (e.g., minced meat) or diets [37]. Oral health status
of LTC populations is rarely investigated, but problems
are
prevalent
with
estimates
of
37%
of
residents
reporting a dry mouth and 51% having untreated cavities
[38]. The resulting pain and distress of poor oral health
has been shown to affect food intake and malnutrition
[21, 37]. In addition, difficulty accessing food due to
packaging, lids, and dishes causes stress at mealtimes
and is associated with poor food intake [30, 39].
Agitation and decreased ability to eat independently also
result in decreased consumption [40]. Requiring assist-
ance commonly results in inadequate food intake [7, 21,
39]. Assistance with food intake ranges from setting up
the meal and opening packages, to encouragement, to
partial assistance with some foods or total assistance for
those unable to eat independently. For those requiring
assistance, food access is influenced by the number of
qualified, trained care staff, the number of residents
requiring assistance, the type of assistance needed, and
the presence of family, volunteers or paid meal helpers
who
can
provide
one-on-one
assistance
[22].
For
instance, a staffing ratio of 3:1 (residents:staff) as com-
pared to 5:1 significantly improves energy intake for
residents in LTC settings [15]. It is anticipated that as
many as 50% of LTC residents requiring total assistance
could consume at least some of their food independently
if changes were made to the environment and supports
were put in place to promote resident autonomy [23, 41],
such as sufficient time to eat [22].
Mealtime experience is operationalized as the physical
and psychosocial mealtime environment and mealtime
processes that can influence food intake [42]. Mealtimes
can be the highlight of the day for residents, providing
opportunities for social interaction as well as develop-
ment of relationships with care providers and tablemates
[43]. Apathy and depression have been found to be inde-
pendently associated with weight loss in LTC residents
[44] and may be influenced by a negative mealtime
experience [42]. Two theories specific to persons with
dementia (Mealtimes as Active Processes in LTC and the
Life Nourishment Theory), demonstrate the importance
and potential influence of the psychosocial environment
on food intake [28, 45]. It is hypothesized that positive
social connections and honouring individual identities
(e.g., food preferences) at mealtimes will promote food
intake and quality of life. Consistent with these theories,
family style dining provides greater opportunity for
social interaction and choice of food offerings and has
been shown to improve energy intake in LTC [46–48].
The Eden Alternative®, which is a ‘household’ model pro-
viding care to a small number of residents in a resident-
centred and homelike setting has some benefit with
respect to maintaining body weight [49]. This associ-
ation may be linked to tailored individualization of care
including participation in food preparation, meal choices
and honoring food preferences. Social models of care
that recognize and encourage staff-resident relationships
and resident-centered care are seen as the preferred
approach to promote residents quality of life in LTC [49,
50]. To date, the relational and resident-centeredness of
mealtimes has been qualitatively explored [43, 47, 51,
52], but has yet to be quantitatively assessed to deter-
mine its association with food intake. The physical
environment can also influence eating [41, 53] and
‘homelike’ environments with music, decorations, and
table dressings have been shown to improve residents’
food intake [46, 54]. Food being plated on the unit,
rather than in the kitchen, enhances consumption [54];
residents see and smell the food and can interact with
staff about choosing their meal and desired portions at
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 3 of 15

the point of service. On the other hand, bulk delivery
systems can create noise and distraction [39].
In
sum,
we
lack
rigorously
measured
dietary
consumption data to understand the prevalence of
inadequate food intake in LTC residents. Multi-level
determinants that address meal quality, meal access
and mealtime experience have yet to be collectively
assessed to understand their relevance to food intake.
Without this knowledge, we cannot undertake the
development
of
effective
interventions
and
policy
directives to address the high prevalence of LTC
malnutrition and its costly human and system conse-
quences. Specifically, determinants amenable to an
intervention, which could have strong potential to
improve food intake, have rarely been explored. The
objectives of this cross-sectional, multi-site study were
to:
1. establish the prevalence of inadequate energy, protein,
micronutrient and fluid intake of residents in LTC,
across and within four Canadian provinces, and;
2. identify the independent and inter-related associa-
tions between multi-level (i.e., resident, unit, LTC
home, province) determinants of energy and protein
intake of residents in Canadian LTC homes.
The purpose of this paper is to fully describe the
protocol used to rigorously measure food intake and to
examine determinants of food and fluid intake among
older adults participating in the Making the Most of
Mealtimes (M3) study.
Methods/design
M3 is a cross-sectional, multi-site study with data collec-
tion in four provinces in Canada: Alberta, Manitoba,
Ontario and New Brunswick. Regional variations in
policy and funding exist within the LTC sector in
Canada [55, 56], and thus inclusion of diverse regions
supported obtaining a more comprehensive understand-
ing of determinants of food intake. Homes within each
province were purposively sampled to also attain diver-
sity with respect to size, model of care, profit-status,
cultural factors, rural/urban region and other home-level
determinants that could impact food intake [15]. Sample
size estimation was based on both objectives, and con-
sidered the effect of provincial and home clusters and
the
intra-class
correlation
(ICC)
between
residents
within the same facility. Based on previously collected
energy intake data from two Canadian studies involving
several homes, the ICC (ICC 0.17 and 0.04, with aver-
ages of 1511 and 1180 kcals, respectively) [8, 57] was
used to determine that a sample of 20 residents in each
of 32 homes would provide sufficient sample size to esti-
mate energy intake with a 95% confidence interval of
plus or minus 56–58 kcals for the entire sample, and
plus or minus 112–116 kcals for provincial samples.
Hierarchical multiple linear regression will be used to
identify the independent determinants of energy and
protein intake of residents. Sample size in regression
modeling is dependent on many factors including covar-
iate measurement error [58]. To attain our final sample
size we recognized that multi-level models involving co-
variates at the LTC home level require an increased
number of homes rather than an increased number of
participants within homes. Previous LTC research using
multi-level regression analyses with food intake determi-
nants found 714 residents sufficient for multivariate
modeling across levels (LTC home, resident) [15]. Based
on funding constraints, the final sample recruited for
this data collection was 20 residents per home, from
eight homes in each province (n = 32 homes), for a final
sample of 639 residents (1 participant withdrew con-
sent). Data were collected at the resident, dining room,
care staff and home level for this study. Researchers
were in the home for an approximately 1-month period
to collect all data. Figure 1 provides an overview of the
sample and flow of data collection.
Participants
Eligible LTC homes had been in operation for at least
6 months; had a minimum of 50 residents meeting
resident eligibility criteria as described below; and had
unique characteristics that promoted home diversity in
the
sample
(e.g.,
rural,
cultural
emphasis,
size,
continuum of care), including profit structure (for profit
n = 10, not for profit homes n = 22). Within each LTC
home,
we
recruited
residents
from
one
to
three
randomly selected care units (depending on size) where
a care unit was defined as: 1) a geographic area in a LTC
home, 2) having a consistent, assigned group of direct
care staff and 3) its own dining area, if possible. One of
the selected units per home was a dementia specific
unit, if such a designation existed for the home, to
ensure the inclusion of persons with dementia in the
sample. All residents within these units, regardless of
cognitive ability, were eligible to participate if they were:
over the age of 65 years (one participant was 62 years);
required at least 2 h per day of care due to dependence
in activities of daily living (e.g., bathing); resided in the
home for at least 1 month; and, either they or their sub-
stitute decision maker provided consent to participate.
Residents were excluded if they were currently medically
unstable; were a temporary resident in LTC (i.e., respite
or convalescent care); required tube feeding; were not
eating anymore because they were at the end of their
life; did not routinely eat in the dining room; or had ad-
vanced directives excluding them from research partici-
pation. Care staff was trained on these criteria and
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 4 of 15

identified those residents who were eligible [8]. Eligible
residents from each unit were collated and a random
number table was used to identify the order in which
residents should be approached by the trained care staff
member to determine if they would be interested in
hearing more about the study from the researchers. An
alternative decision maker (ADM) was approached for
residents who had a Cognitive Performance Scale (CPS)
from the Minimum Data Set that indicated they had
moderate/high cognitive impairment (i.e., 3+) or where
the
care
staff
believed
the
ADM
should
also
be
approached. Training and standardized scripts were pro-
vided to the care staff to support recruitment. To deter-
mine representativeness of the participants for their
units, age, gender and cognition (CPS) were noted for all
eligible residents. Care staff was eligible to participate if
they were employed by the home (fulltime/part time
position), they were direct care staff (e.g., nursing, recre-
ation, dietary aids) and if they worked on the selected
study units on the days of this data collection.
Ethics approval and consent to participate
Ethics approval for the study was obtained from the
Research Ethics Boards of the Universities of Alberta,
Manitoba, Moncton, Waterloo and the University Health
Network, Toronto. Some LTC homes had an individual
review committe, and if so, ethics approval was sought.
Informed written consent was obtained by the researchers
directly from residents who had the capacity to consent, or
from their ADM. Many of the initial recruitment meetings
between the researcher and ADMs were conducted over
the telephone. The researcher described the study proce-
dures in detail verbally and if the ADM was comfortable
with this information, they had the option of providing
verbal consent immediately. This was followed with the
mailing/emailing of the information letter and consent
form to the ADM and having the written consent returned
by mail or email. Residents who were able to provide their
own consent did this in writing at the time of recruitment.
During data collection, we continually evaluated assent to
participate in the study by the willingness of residents to
cooperate with various data collection procedures [59].
Care staff provided informed consent to complete their
questionnaire and the home signed a research agreement
noting their cooperation with all aspects of the study
including completion of a home survey.
Measures and data collection
Data were collected between January and December
2015 in each of the 4 provinces. Provincial coordinators
were extensively trained face-to-face on all procedures;
three of four of these researchers were registered
Fig. 1 Overview of Data Collection
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 5 of 15

dietitians, while the fourth had been trained in dietetics
and had extensive applied nutrition research experience.
These coordinators subsequently trained two research
assistants (RAs; post graduates, typically health students)
per province to collect food intake and meal observation
data for the resident participants. All of the data was
collected either at the home, dining room, staff or
resident level.
Home/staff level measures
A home-level questionnaire was used to collect variables
influenced by provincial policy (e.g., food budget alloca-
tion, dietitian clinical time) and home-level policy and
practices (e.g., staff training, menu planning process)
and characteristics (e.g., age of home, size, type of food
production). This questionnaire was provided to the
director of care and the food services manager at the be-
ginning of the study. In addition, the food service man-
ager was asked to provide the full menu for nutrient
analysis and an assessment of variety (first week of the
planned menu, regular and pureed first choice; variety
only for regular menu). Home recipes and specific food
products (including manufacturer nutrient analysis), as
well as standard portion sizes for estimated foods, was
used to complete an accurate nutrient analysis. This ana-
lysis was completed on site by the RAs in the event that
they needed to direct questions to the food service man-
ager on menu specifics (e.g., type of margarine, meal
substitutions). Table 1 provides further detail about the
home questionnaire.
A minimum of 10 care staff on the study units
completed the valid and reliable Person-Directed Care
measure [60, 61], which collects staff perception of
resident-centered care and organizational support with
50 items. Demographic information was also provided.
To reimburse their time a $5 gift card for a national
coffee shop was provided upon completion. Although
we had planned to have care staff complete this data
collection on laptops, almost all staff preferred a paper
and pencil version of the questionnaire.
At the unit level, the physical and psychosocial dining
environment was assessed. The Dining Environment
Assessment Protocol (DEAP) is a face valid and inter-
observer reliable instrument that assesses key design fea-
tures and rates the dining space on homelikeness and
functionality with summative scales (1 = low, 8 = high)
Table 1 Home-level variables collected in the Making the Most of Mealtimes Prevalence Study
Home Level Characteristics
Staff perceptions of Person Directed Care (50 items)
Food production system
How food is purchased (e.g., purchasing group)
Profit/non-profit status
Proportion of food commercially
produced
Monthly food cost per resident and if included oral
nutritional supplements
Home provides various care levels in one setting eg.
Independent living, retirement, long term care
How modified textures produced
or if purchased
Cost of oral nutritional supplements per month
Age of home
Production of thickened fluids
Vitamin/mineral supplements included in raw food
cost and approximate cost per month
Types of care provided in home and number of beds
Timing of meals/snacks
Weighing procedures for residents, type of scales
available
Staffing levels (nursing, dietary aids/food service
workers, cooks)
Multiple seatings for meals to
accommodate all residents
Training of food service staff
Specialized staffing levels (chef, director of food
service, clinical dietitian, speech language pathologist,
occupational therapist, recreational therapist)
Main meal of the day
Food safety monitoring and training of staff
Access to specialized services (e.g., dentist/dental
hygienist)
Availability of food and fluid outside
of meals
Quality improvement initiatives in the past year
Who involved in menu planning (including
residents/families)
How special occasions observed at
meals
In-service training for food service staff
Latest revision of menu
How seating arrangements at meals
determined
Policies and procedures for sickness with food
service employees
Standards for menu planning
Ability to store food in resident’s
room or common fridge
How need for modified textures determined
If and how the menu is seasonally changed to
accommodate available foods
Ability to order in restaurant food
from outside of the home
Availability of food/nutrition support services
How resident food choice determined
Ability to have meals delivered to their
room (and if additional cost required)
Areas of improvement in nutrition, dining, meal
service desired by the home and barriers to
accomplishing
Food delivery system
Eat with family in the home
Diet (e.g., diabetic) options available
Keller et al. BMC Geriatrics  (2017) 17:15 
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[62]. The DEAP was completed once by the provincial
coordinators, when the dining areas were empty, for
each of the 82 dining areas included in the study across
the 32 homes. A second measure, the face valid and inter-
observer reliable Mealtime Scan (MTS) [63], was com-
pleted 4–6 times over the duration of data collection in a
home, by coordinators in each dining space when occu-
pied, with all meals (breakfast, lunch and supper) repre-
sented at least once. Number of persons, ambiance (e.g.,
table dressings), physical environment (light, temperature,
and noise with an environmental meter (Shimana SE-DT-
8820) using a standardized protocol) [64] and Mealtime
Relational Care checklist (M-RCC) for care staff mealtime
practices were observed and tallied/scored. This latter
scale focused on care staff practices that were dignified
(e.g., using napkin to wipe mouth), supported resident
participation in the meal (e.g., clearing plates), promoted
social interaction among residents and care staff, and
attended to key hospitality concepts (e.g., not talking over
residents to other staff, clearing tables promptly); positive
and their contrasting more negative behaviours were
tracked. Three summative scales (1 = low, 8 = high) on the
physical environment (e.g., light, sound, temperature), so-
cial, and person-centredness of the entire dining environ-
ment were completed at the end of each meal after all
observations on the MTS were made; individual variables/
sections on the MTS were used by coordinators to rate
these aspects of the meal. The ratings for the 4–6 MTS
observations conducted in each dining room over the dur-
ation of the study in a home were summed to determine
an average for the dining room. Table 2 provides further
detail on these measures.
Resident level measures
Extensive data at the resident level were collected for
health, nutritional status and potential risk factors for poor
food intake. Only key aspects are presented in the text as
Table 3 outlines measures in detail. For almost all residents,
three nonconsecutive food assessment days, including a
weekend day, were used to determine food intake of partic-
ipants; for a few residents, logistics required consecutive
days being observed. Each food item on the main plates
was weighed before and after the meal to determine the
amount consumed by subtraction and food wastage due to
spillage was estimated, where possible, for the unconsumed
portion [65]. Fluids and side dishes were estimated, with
the amount determined from the detailed production
menu, as well as by measuring vessel/cup size prior to data
collection. Two RAs completed this data collection on five
residents per meal per unit. If a resident was not present
on the unit for the meal, an alternative day was chosen to
promote as complete a data collection as possible. RAs also
estimated food and fluid intake for the participating resi-
dents at snack times and between meals, identifying source
(i.e., food brought in by family/resident/staff) and con-
sumption by observing and/or asking the residents, family
and/or staff. Care staff on the unit were asked to report any
additional food intake such as before-breakfast food con-
sumption, and were trained to record the consumption of
evening
snacks
and
beverages
on
the
food
intake
Table 2 Dining room variables collected in the Making the Most of Mealtimes Prevalence Study
Dining Environment Assessment Protocol (DEAP) [62]
Type of unit (e.g., segregated dementia)
Diagram of physical layout
Rating of lighting intensity and glare
General physical space e.g., # chairs, tables,
staff stools, entryways, garden/outside views
Components that promote safety
e.g., contrast, rounded edges of furniture,
adjustable tables, secured toxic substances,
short distance from bedrooms
Components that are homelike/promote orientation
e.g., dining room open between meals, clock, accessible
kitchen for residents/families, television, posted menu
Residents opinions on noise, light,
temperature responded to
Size of space rated on homelikeness
Pathways rated on length for meal delivery, and safety
Presence of obstacles/clutter in dining area
Functionality of space so that staff can
view all residents
Use of restraints (lap, chair)
Mix of seating arrangements (e.g., 2, 4, etc.
tablemates)
Overall rating on homelikeness
(1 = low, 8 = high)
Overall rating on functionality of space (1 = low, 8 =
high)
Mealtime Scan [63]
Temperature, luminescence, humidity,
sound (@ 4 ×/meal)
Number and types of persons in dining
room and adjacent areas if eating
Number eating in own room
Any food production/delivery issues (e.g.,
change in menu, problem with food,
short staffed)
Orientation cues e.g., food odour, clock,
table decorations, table cloth/settings,
contrast
Residents involved in mealtime activities
Noise that could be distracting and source
e.g., crushing medications, hallway
chatter etc.
Television, music (source and type,
loudness level obstructed conversation)
Mealtime-Relational Care Checklist (n = 26 positive and
negative items)
Overall rating of functionality of physical
space (low = 1, high = 8)
Overall assessment of social space
(low = 1, high = 8)
Overall assessment of person centred care
(low = 1, high = 8)
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 7 of 15

assessment day. Other mealtime behaviours (e.g., time to
eat, number of assistants helping the participant etc.) were
recorded at each meal.
At one meal per food intake assessment day for a
resident, a more elaborate mealtime observation was com-
pleted. This included the Edinburgh Feeding Question-
naire
(Ed-FED)
which
scores
eating
challenges
and
assistance required [66]; 9 additional items that describe
further eating challenges (e.g., does the resident get dis-
tracted, do they cough during the meal, choke etc.) were
also recorded and scaled to be consistent with Ed-FED
(never, sometimes, frequent). Two further tools were used
to assess the interactions between care staff and the resi-
dent at this meal. For those residents requiring total eating
assistance to complete a meal, the Relational Behaviour
Scale [67] was also completed. The M-RCC checklist from
the MTS (described above) was completed on all individ-
ual resident participants, regardless of requiring eating as-
sistance, to observe interactions between care staff and the
participant. Averages across meals will be used in analyses.
Nutritional status was assessed with Subjective Global
Assessment [68]. The Patient-Generated version, originally
designed for oncology outpatients [69] provides more land-
marks for a physical exam as well as risk factors for poor
food intake. As many residents are unable to be an inform-
ant, sections typically completed by the patient (e.g., weight
change, risk factors for food intake) were gathered from the
chart, staff or family members. Source of information was
tracked. The Mini- Nutritional Assessment Short Form
was also used to determine malnutrition risk [71].
The interRAI LTCF [71] provides a standardized and
validated means of collecting comprehensive clinical
information on LTC residents and scales for cognition
(CPS), pain, activities of daily living, depression (Depres-
sion Rating Scale - DRS), and challenging behaviours
(Aggressive Behaviour scales - ABS) [72–76] can be
derived. Items to complete these scales as well as the
oral health/nutrition items were collected by provincial
research coordinators who interviewed care staff cur-
rently providing care to the resident. Care staff were
asked to reflect on the last 3-days for reporting needs
and behaviours of the resident.
Dysphagia risk was a composite variable. If the resident
was on a thickened fluid diet, they were deemed to be at
‘dyphagia risk’. For all other resident participants, a
screening procedure was completed to determine risk for
swallowing problems. The provincial coordinator (or in
the case of Ontario, a doctoral student who was a speech-
language pathologist) completed the standardized Screen-
ing Tool for Acute Neuro Dysphagia (STAND) [77]. This
brief screening protocol has been validated against a
modified barium videofloroscopy and has 92% sensitivity
and 60% specificity for detecting aspiration [77]. For this
protocol, residents consumed three teaspoons of apple-
sauce and drank 90 ml of water, in a continuous fashion;
signs of dysphagia (i.e., coughing, wet voice quality, throat
clearing) were noted. If the participant passed the STAND
without signs of swallowing difficulty but later displayed
coughing or choking during meal observations (as cap-
tured through the additional eating challenges questions),
they were considered to also be at risk for dysphagia.
Dentition and oral health were determined by a stan-
dardized oral assessment based on the Canadian Health
Measures
Survey
[78].
In
each
province
a
single
Table 3 Resident-level variables collected in the Making the Most of Mealtimes Prevalence Study
Resident Data Collection
Date of birth, gender, ethnicity, months
since admission
Food brought in/purchased by resident/family
Resident Food and Foodservice Satisfaction Survey
[79]
Weight history for past 6 months
Family provides micronutrient supplements
Smell screening with Sniffin Sticks (n = 12 odours)
[78]
Diet/fluid texture prescription
Mini-Nutritional Assessment- Short Form (screen
for nutritional risk) [70]
Relational Behaviour Scale [67]
Other therapeutic diet prescription
Knee height (to estimate standing height)
Weighed food intake, main plate; estimated side
dishes/beverages
Cultural meal preferences met
Ulna length (to estimate standing height) [87]
Between meal snacks/beverages estimated
Use of oral nutritional supplements
Calf circumference
Ed-FED (× 3 meals) [66] and 9 additional eating
challenges
Diagnoses (based on InterRAI
LTCF categories) [71]
Patient Generated- Subjective Global Assessment
to assess malnutrition [69]
Mealtime-Relational Care Checklist (× 3 meals) [63]
Medication (dose, frequency)
STAND (dysphagia screening instrument) [77]
Time in dining room, taken to eat (× 9 meals)
Use of antibiotics,
psychotropics, vitamin/minerals
Oral health exam (e.g., teeth count, observance
of problems, pain, opinion on need for urgent
dental care, oral health affect food intake) [78]
Number of assistants during meals and whom; served
or assist with eating (× 9 meals)
Any acute change that could affect
food intake
InterRAI LTCF (selected items) [71]
Leaving dining room during meals/wandering
Keller et al. BMC Geriatrics  (2017) 17:15 
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contracted and trained dental hygienist experienced with
the LTC population completed this standardized oral
assessment for the participating residents. For residents
with adequate cognition, olfactory ability was assessed
using twelve “Sniffin’ Sticks” (Burghart Messtechnik
GmbH) [79]. Participants sniffed with each nostril an
olfactory ‘pen’ containing a known smell and attempted
to identify the odour from a list of four pictures with
word labels (e.g., lemon, coffee). Satisfaction with food
and foodservice was also completed by these residents
using a 21-item interviewer-administered tool, valid and
reliable for persons with and without dementia [80].
Residents with mild/moderate dementia were asked to
complete these assessments (i.e., CPS 1–3); if the provin-
cial coordinator perceived difficulties with completion
(i.e., resident needed multiple prompts to answer the
question, forgot the question), the assessment was
stopped.
Data management and analyses
REDCap™(Vanderbilt University, Version 6.2.2), a data
entry/capture system was used to promote data security,
and resident and staff data was sent in this electronic for-
mat to the study centre at the University of Waterloo. An
information specialist was contracted to develop the RED-
Cap™forms and served as a consultant throughout the
project until all data collection was completed and data
were cleaned, exported and merged. Paper and pencil cop-
ies of forms were used when computer collection was not
possible (e.g., meal meal observations, patient assess-
ments, home survey ). Many LTC homes did not have ac-
cess to WiFi, thus encrypted laptops were used during
data collection to promote security. A protocol was
followed for routine downloading of data to a secure ser-
ver at University of Waterloo. Hard copy data were kept
in a secured file box and kept in locked storage when not
in use. Data were cleaned using an extensive procedure by
the lead provincial coordinator and an analyst; data check-
ing was completed where necessary with sites (e.g., home
questionnaire). Food Processor™software (ESHA Re-
search, Version 10.14.1) using the Canadian Nutrient File
was used for nutrient analysis of food intake and menus.
Recipes from homes were also inputted into the system.
The Food Processor files were sent through a secure file
transfer process to University of Waterloo for extraction
of nutrient information and assembling into the main data
set. Home survey, dining room observations and staff
questionnaires were inputted into the resident file to en-
sure that all data were merged at the individual resident
case level in the data set.
The data were collected at multiple levels and are con-
sidered ‘clustered’ at the province, LTC home and dining
room
levels.
Thus,
multivariate
models
including
variables from more than one level (i.e., home, unit,
resident) will require nested or hierarchical analysis.
Prevalence of inadequacy of micronutrient intakes will
be evaluated through comparison of intakes to the
Dietary Reference Intake using the Estimated Average
Requirement cut-point method [81]. This analysis will
take
into
account
the
inter-
and
intra-individual
variation inherent in dietary assessment [82]. Analyses
will be completed with and without micronutrient sup-
plements. Analyses relating dietary energy and protein
intake (outcomes) to potential determinants will be
based on the theoretical M3 model. Initial analyses will
be conducted by level to screen for important relation-
ships using hierarchical multi-level regression models.
These models will contain indicator variables for prov-
ince, and a random effect for homes and units, as well
as
the
independent
variable
under
consideration.
Variables with p < 0.2 will be retained to develop multi-
variate models. Retained home, dining room, staff and
resident-level variables, will be incorporated into the
final multi-level models. Collinearity and interactions
will be assessed in final models. Backwards elimination
will be used to develop the final multi-level models. A
variety of descriptive, bivariate, and multivariate analyses
are also planned based on key questions posed by the
co-investigators to make full use of this exceptional
dataset (e.g., Is dysphagia risk associated with time to
eat, oral health etc.).
Discussion
This unique study not only has resulted in a rigorous
data collection that will be invaluable for understanding
food intake and care with respect to nutrition of resi-
dents in LTC, but also provides important insights for
researchers on the importance of cluster design and
analysis in understanding these questions. The following
discussion further elaborates on what has been learned
as a result of conducting this study so far.
Building research relationships with homes and staff
A significant barrier to LTC research is receiving consent
from homes, staff and residents to participate in the data
collection. The best way to ensure participation is to
build a trusting relationship [83]. We recruited 30 of 32
homes purposively at the time of submitting our pro-
posal for funding; each provincial investigator leading
the project in their province conducted this recruitment
based on their prior relationships with these sites. All of
these initially recruited homes participated in the pro-
ject. Confidentiality of home identity was also assured to
promote feelings of trust [83] and a memorandum of
understanding between the university and the individual
LTC
homes
was
completed.
This
agreement
was
reviewed and signed by home management in a face-to-
face meeting with the researchers and/or the provincial
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 9 of 15

coordinators
in
each
province
approximately
two
months prior to starting recruitment of participants in
that home. Facility management, director of care (nurs-
ing), food service management and the clinical dietitian
were involved in these initial meetings and all signed this
agreement to ensure good communication about the
study. A primary contact person for each home was
determined and this person was involved in all commu-
nications that supported the data collection. In most
LTC homes, this role was assigned to the director of
food service, but in other homes, it was the clinical
dietitian. Post data collection, a summary document
described nutrition risk, risk for dysphagia, oral health,
and any other concerns for each participant. This sum-
mary was provided to the home’s dietitian and primary
contact person to support the resolution of these poten-
tial problems. As well, most homes were interested in
their menu nutrient analysis and this information was
provided to them within a month of the data collection.
When the data collection was finished across the country,
home representatives were invited to a stakeholder meet-
ing, which included government representatives, decision
makers/knowledge users and other local LTC homes to
present site-specific anonymous data on key variables for
the province. This helped to build awareness of the study,
to start work with respect to improving provincial policy,
and to begin intervention development. In separate indi-
vidual face-to-face meetings, each home was provided
with their specific results, benchmarked
with
other
homes in their province. All homes were receptive
to this engagement and expressed appreciation for
being involved in the study and receiving their own
data to support quality improvement initiatives. As a
result of these relationship building efforts, many
homes have expressed interest in being part of further
research focused on developing and testing interven-
tions to improve food intake of residents.
As the researchers were in the home for approximately
20 days in a month, a clear understanding of their pur-
pose and the expectations for staff around resident data
collection were needed. A variety of strategies were used
to promote this communication and build positive rela-
tionships. Management discussed the project with staff
on the study units, using a script provided by the
researchers. A similar script was provided for communi-
cation books. The provincial coordinator was the first
researcher to enter the unit and started with training the
seconded home staff to determine resident eligibility.
They were typically introduced to key staff members by
the home contact person and procedures for reviewing
resident charts were discussed. DEAP was conducted on
the first or second day by the coordinator as a way of
initiating data collection for the units. All researchers
wore nametags and were trained to be as unobtrusive as
possible. The provincial coordinator was on site the first
day that the RAs entered the home; it was found to be
beneficial if the RAs could be introduced to key staff
members and shown around the home and the study
units the day before they began mealtime observations.
The researchers worked as a team to identify residents
and ensure a complete data collection. However, staff
cooperation was needed for help in initially identifying
and finding participating residents as they were often
not in their rooms and arm bracelets for identification
were not typically used. Where staff was needed for resi-
dent data collection (e.g., risk factors for poor food in-
take as reported by care staff for the PG-SGA), care staff
were asked when a convenient time would be for this
brief data collection. Where more intensive time was re-
quired (e.g., interviewing care staff for completion of
interRAI LTCF), the home provided an additional staff
member for coverage and this cost was billed to the pro-
ject. All of these activities helped to build a positive rela-
tionship with the staff and supported a quality data
collection from residents.
Staff participation was promoted by providing back-fill
for staffing on selected days when the staff PDC ques-
tionnaire was completed. Staff also appreciated the $5
gift certificate. As discussed, most of the staff members
were hesitant to use the provided laptops for their data
entry and hard copy versions of the questionnaire were
used. They required additional assurance that their indi-
vidual results would remain anonymous and in no way
would home management know who participated. When
results were presented to management, staffing classifi-
cation of ‘nursing’ or ‘other’ was only used to promote
anonymity of staff that was limited in numbers (e.g.,
recreation staff).
Recruiting residents
Homes were chosen to have a sufficient number of resi-
dents (~50) to ensure that 20 eligible residents could be
recruited. In some sites, random selection of the unit to
recruit resident and care staff participants was not
possible due to the size of the home. A care staff mem-
ber, typically a regulated nurse (RPN/LPN), was shown
how to: a) develop the list of eligible residents from the
randomly selected study units, b) randomly select resi-
dents and/or the alternative decision maker (ADM) to
be approached using a random number table, c) use a
script to speak with the resident/ADM, and d) note rea-
sons for lack of interest in the study. This training took
a significant amount of time on the part of the provincial
coordinator and specialized forms, tables and written
instructions were provided to the home to make it as
error-free as possible. The staff member randomly
recruited 40 participants, as we had determined from a
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 10 of 15

pilot that approximately 1 in 2 residents/ADM will
refuse participation. This took a significant amount of
time; the home invoiced the research project for the
hours required by the seconded home staff member who
completed this work.
Many homes struggled with this first step of data
collection and we did not get complete records on par-
ticipation rate as a result. However this two-step process
for attaining consent resulted in only minimal problems
with withdrawal post recruitment. Based on records
available from homes, 84% of eligible residents/ADM
reached by home staff agreed to be contacted by re-
searchers. Not all eligible residents were contacted by
the researchers; if they reached the 20 patient quota per
home, the remaining list of eligible participants who had
agreed
to
be
approached
by
researchers
was
not
exhausted. As resident participation rates for Canadian
LTC research ranges from 65–72% [83–85], M3 had a
successful participation rate which is attributed to the
relationship building, training of home and research
staff, as well as the non-invasive protocol. Participants
were representative of the units where they lived. Specif-
ically mean age (86.9 SD 7.81 vs. 87.0 SD 8.2 respect-
ively), and proportions of males (male 32% v. 31.5%
respectively) or those requiring an ADM for consent
(74.6% vs. 72.8% respectively) did not differ between par-
ticipants and eligible non-participants. Attrition due to
death was minimized by recruiting and collecting all
data within one-month. Ten eligible residents died be-
tween identification by the home staff (screening) and
recruitment by the researchers, while six participants
died during data collection. These latter participants
were retained however, as they each had a minimum of
two days of observed food intake, and thus provided key
data for analysis. Where prudent, sensitivity analyses will
be used to determine if these patients unduly affect in-
terpretation of findings.
Data collection
We opted for tools and scales that had a minimal bur-
den for residents to promote a complete data collection
and chose where possible tools that were familiar to
homes and staff (e.g., interRAI LTCF). As a result, we
achieved high completion rates for most resident-level
measures: PG-SGA (99.8%), calf circumference (98%),
STAND (69.7%; 398/517 not on thickened fluids), oral
health exam (89.8%; 574/639, 559 (87.5%) with complete
data on exam), food satisfaction (52.3%), and smell test
(47.3%). The latter two were expected to be only com-
pleted in those residents with sufficient cognition to do
so, which limited the sample for these measures. STAND
was not completed for residents that consumed thick-
ened fluids (10.6%), as we did not want to expose these
residents to the risk of aspiration; the remainder of
resident participants where STAND was not completed
either refused participation or were unable to follow the
simple commands required. The proportion that com-
pleted STAND demonstrated the importance of an a
priori decision to create a composite variable to identify
dysphagia risk, which would ensure 100% identification
of this important covariate. Dysphagia risk was defined
as: thickened fluid prescription or failed STAND or ex-
hibited any coughing or choking during three observed
meals.
As noted, data collection on food intake was rigorous
with protocols for weighing and estimating food clearly
outlined and followed by the RAs. There were some
challenges in attaining non-consecutive days or timing.
Specifically, where travel from the provincial research
centre required an overnight stay, the nonconsecutive
day requirement for meal observation could not always
be adhered to. We were able to observe 6+ meals for al-
most all participants (99%) with only 6% of participants
having one or more meals that were not observed as
they were consumed outside of the home. With a mini-
mum of two days of intake for all residents, and little
missing data for other days, we consider this data suffi-
ciently thorough to address the primary research ques-
tions. The poorest collection on food intake was the
evening snack as estimated by care staff. Other than
using reminders with evening staff as well as clear in-
structions and daily follow up, no further suggestions
can be provided to support complete and accurate data
collection when RAs are not in the home to observe
food intake. However, as 80–90% of calories are con-
sumed during meals in LTC [86], we anticipate minimal
bias in our primary outcomes due to inaccuracy in col-
lection of consumption of food in the evening (after sup-
per) or overnight. Nutrient analysis protocols promoted
rigor and consistency. If a home recipe was unavailable,
the RAs were trained to use the following steps: 1) use
another home’s recipe for a similar food item in their
province; if this was unavailable, 2) use a recipe from an-
other province; and if this was not available, 3) consult
Food Processor™standard recipes and other sources as
required, checking ingredients with the home’s cooks
and/or food service management.
RAs were trained to be as unobtrusive as possible. As
care staff in the dining room was not aware of the spe-
cifics of data collection and only observed the RAs
weighing residents’ food, we anticipate that care staff’s
behaviours towards residents were unchanged. Multiple
measures for mealtime scans and meal behaviour obser-
vations were averaged with standard deviations reviewed
to determine any potential outliers.
There were relatively few challenges with all other data
collection measures. Intensive in-person training of pro-
vincial coordinators and RAs was extremely important
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 11 of 15

for ensuring quality of the data collection and data entry.
All LTC homes submitted their survey and we surpassed
care staff completion of the PDC questionnaire (goal:
320, n = 480 submitted), with three quarters being care
staff with direct care responsibilities for residents (e.g.,
nursing). The small incentive was helpful in recruiting
this number of care staff as was allowing staff to
complete a paper form rather than relying solely on the
REDCap™form on the laptop. All DEAP and MTS were
completed as per protocol, excepting two small dining
lounges where only one MTS was completed rather than
four. All research staff was in contact via a virtual inter-
net group to promote communications. For example,
RAs were encouraged to routinely communicate around
nutrient analysis questions to promote consistency.
Monthly teleconferences were held for the provincial
lead investigators and provincial coordinators as a
national
group
to
share
challenges
and
ensure
consistency in data collection province-to-province. The
provincial coordinator periodically reviewed the data
collection at meals completed by the RAs. The primary
investigator also routinely reviewed the data on RED-
cap™to ensure consistency and accuracy.
A significant amount of time was spent by the lead
provincial coordinator and a research analyst in cleaning
and checking the data that was submitted on RedCAP™.
Specifically, PG-SGA was checked by having the lead
investigator review randomly selected participants from
all provinces to ensure consistency in malnutrition
classification based on the charted components of this
measure, as this instrument does require clinical judg-
ment. Some sites also found it challenging to report care
staffing levels on the home survey. Checking was done
in the data cleaning phase by confirming values with a
home informant.
Study feasibility
Pilot data were collected prior to the main study to con-
firm feasibility of all measures and the time necessary to
complete the data collection in home. In each home, RAs
were present for approximately 10–11 h per day for 15–
20 days over a one-month period to collect residents’ food
intake data. This was the most laborious and expensive
portion of the data collection. They typically worked 3–4
days per week and had up to three, one-week breaks dur-
ing the 1 year of data collection. Two provinces were able
to complete data collection within ten months as it was
the preference of the research staff to take fewer breaks.
Two provinces did require overnight stays for research
staff due to the commuting distance to the provincial
research centre; the research budget accounted for these
additional costs. RAs did have time between meals to
conduct nutrient analysis of resident intake and menus;
however, some ‘administrative’ days in each province were
required when RAs were not in a LTC home to catch up
on key aspects of data entry and cleaning. Cohesiveness of
the provincial teams ensured data collection with minimal
challenges, considering the scope and the breadth of this
study.
Summary
Poor food and fluid intake is a common but preventable
problem in LTC. Inadequate intake leads to malnutrition,
and with its high prevalence in LTC there is a subsequent
significant impact on the quality of life, health, care, and
costs for residents in LTC. The anticipated increase in
need for LTC residences over the next three decades [83],
as the baby-boomers age, necessitates solutions to the
problem of poor food intake in LTC. Yet, effective solu-
tions will continue to be elusive until this problem is fully
described, including understanding the determinants of
food intake. Multi-level data collection is needed to fully
characterize the problem of inadequate food intake and
this study will provide the necessary evidence to support
improvements in policy and practice. The M3 study with
its basis in an evidence-based conceptual framework, will
address many of the current gaps in our understanding of
this complex problem and provide a way forward to devel-
oping feasible and cost-effective solutions to poor food
intake and malnutrition in LTC.
Abbreviations
ADM: Alternative decision maker; DEAP: Dining Environment Assessment
Protocol; DRI: Dietary reference intake; Ed-FED: Edinburgh Feeding Scale;
ICC: Intra class correlation; LTC: Long term care; LTCF: Long term care facility;
PG-SGA: Patient Generated- Subjective Global Assessment; M3: Making the
Most Of Mealtimes; MDS: Minimum data set; MTS: Mealtime scan;
RA: Research assistant; M-RCC: Mealime-Relational Care Checklist;
STAND: Screening Tool for Acute Neuro Dysphagia
Acknowledgements
The study would not have been possible without the expertise and
dedication of project managers (Hilary Dunn, Carla Ickert), research analyst
(Jill Morrison), the provincial site coordinators, research assistants, the dental
hygienists, and the interest and cooperation of participating homes,
residents and families. We greatly appreciate their efforts and contributions
to the M3 prevalence study.
Funding
Funding for this project was received from the Canadian Institutes of Health
Research (CIHR), a federally mandated organization to peer review and select
high quality health research. CIHR had no involvement in the study design,
collection, analysis or interpretation of data post the approval of this study
for funding. The PI is an endowed research chair with the Schlegel-University
of Waterloo Research Institute for Aging; half of her salary is provided by this
non-profit organization.
Availability of data and materials
Data collection forms and protocols are available from the primary author.
Data is not available for researchers outside of the co-investigators. This
protocol is registered with ClinicalTrials.gov ID: NCT02800291, retrospectively
registered June 7, 2016.
Authors’ contributions
The Primary Investigator, HK, led the research team and data collection and
developed the initial draft of this manuscript. Co-Principal investigators LD
and CS provided their expertise to specific aspects of the methodology and
Keller et al. BMC Geriatrics  (2017) 17:15 
Page 12 of 15

participated in monthly M3 calls during and post data collection to provide
guidance. Co-Principal investigators SS, CL, and NC led data collection within
their province and supervised local research personnel. All reviewed and
provided input specific to their expertise and experience for the protocol
and this manuscript. Co-investigator KSB provided statistical consultation and
data management support during the proposal and data collection, while
VB, GH, AD, MY, HC, and LV provided expertise in protocol development
specific to their areas of expertise. All authors reviewed and approved this
manuscript.
Competing interests
The authors declare that they have no competing interests.
Consent for publication
Not applicable.
Ethics approval and consent to participate
All study participants or alternative decision makers for residents provided
their written consent to participate. This protocol received clearance from
ethics boards at the University of Waterloo, University of Alberta
(Pro00050002), University of Manitoba (J2014:139), Université de Moncton
(1415–022), and University Hospital Network, University of Toronto
(16-5051-DE). Some individual LTC homes also required ethics review by a
local/regional committee.
Author details
1Schlegel-University of Waterloo Research Institute for Aging, 250
Laurelwood Drive, Waterloo ON N2J 0E2, Canada. 2École des sciences des
aliments, de nutrition et d’études familiales, Faculté des sciences de la santé
et des services communautaires, Université de Moncton, Moncton NB E1A
3E9, Canada. 3Faculty of Nursing, University of Alberta, Edmonton T6G 1C9,
AB, Canada. 4Faculty of Agricultural & Food, Sciences, University of Manitoba,
405 Human Ecology Building, Winnipeg MB R3T 2N2, Canada. 5Toronto
Rehabilitation Institute, University Health Network, 550 University Avenue,
#12-101, Toronto M5G 2A2, ON, Canada. 6Rehabilitation Sciences Institute,
Faculty of Medicine, University of Toronto, Toronto M5G 1V7, Canada.
7Department of Food Science, University of Guelph, Guelph ON N1G 2W1,
Canada. 8Department of Statistics and Actuarial Science, University of
Waterloo, Waterloo, ON N2L 3G1, Canada. 9Department of Gerontology,
Simon Fraser University, 2800-515 W. Hastings St, Vancouver, BC V6B 5K3,
Canada. 10School of Dentistry, University of Alberta, 5-575, Edmonton Clinic
Health Academy, 11405-87 Avenue, Edmonton, AB T6G 1C9, Canada.
11Department of Human Health and Nutritional Sciences, University of
Guelph, Guelph, ON N1G 2W1, Canada. 12Conestoga College, School of
Health Sciences and Community Services, Kitchener, ON N2G 4M4, Canada.
Received: 20 August 2016 Accepted: 16 December 2016
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