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Review
Web-Based Interventions to Promote Healthy Lifestyles for Older
Adults: Scoping Review
Audrey Lavoie1,2,3*, MSN; Véronique Dubé1,2,3*, PhD
1Faculty of Nursing, Université de Montréal, Montreal, QC, Canada
2Université de Montréal Marguerite-d’Youville Research Chair on Humanistic Nursing Interventions, Montreal, QC, Canada
3Research center, Centre Hospitalier de l'Université de Montréal, Montreal, QC, Canada
*all authors contributed equally
Corresponding Author:
Audrey Lavoie, MSN
Faculty of Nursing
Université de Montréal
2375 Chem de la Côte-Sainte-Catherine
Montreal, QC, H3T 1A8
Canada
Phone: 1 514 890 8000 ext 31591
Email: audrey.lavoie.7@umontreal.ca
Abstract
Background: With the aging of the population and rising rates of chronic diseases, web-based interventions could be considered
to support older adults in adopting healthy lifestyles. To date, published knowledge syntheses have focused on quantitative studies
among older adults aged ≥50 years. However, those aged ≥65 years may have different needs to be met by these interventions
because of the biological and physiological changes associated with aging, and qualitative studies could help advance knowledge
in this field.
Objective: The objective of this scoping review is to explore the extent of the literature on web-based interventions aimed at
promoting healthy lifestyles among people aged ≥65 years.
Methods: A scoping review was conducted based on the framework proposed by Levac et al. Six databases (ie, MEDLINE,
CINAHL, PsycINFO, Web of Science, the Cochrane Database of Systematic Reviews, and the Joanna Briggs Library) and gray
literature (ie, Google Scholar and OpenGrey) were searched. The final search was conducted on June 23, 2021. The studies were
selected by 2 persons (AL and ML) independently. The included studies were systematic reviews and qualitative and quantitative
studies focusing on web-based interventions to promote healthy lifestyles in people aged ≥65 years that were published in French
or English between 1990 and 2021. Data were extracted in a table and synthesized based on the conceptualization of web-based
interventions (ie, according to the use parameters, behavior change techniques, delivery modes, and theories). A thematic analysis
was performed.
Results: In total, 20 articles were included in this review, which represents studies focused on 11 distinct interventions. All of
the interventions (11/11, 100%) aimed to promote physical activity among older adults. The number of intervention sessions
varied from 5 to 16, with a frequency from daily to once every 2 weeks. Diverse delivery modes such as electronic diary, video,
and phone call were found. The most used behavior change techniques were instruction, feedback, and self-monitoring. Few
interventions (6/11, 55%) were based on a theory. A favorable trend was observed in increasing physical activity, and 5 themes
emerged that appeared to be central to behavior change among older adults: motivation, support, tailoring, barriers, and perceptions.
Conclusions: This scoping review provides a better understanding of the components of web-based interventions and their
outcomes on the healthy lifestyles of people aged ≥65 years. These findings could provide important guidance for the design and
development of future web-based interventions in this field. Further research is needed to continue the development and evaluation
of innovative and accessible interventions to promote healthy lifestyles among older adults.
International Registered Report Identifier (IRRID): RR2-10.2196/23207
(Interact J Med Res 2022;11(2):e37315) doi: 10.2196/37315
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KEYWORDS
aged; behavior change; components; effects; healthy lifestyle; web-based intervention
Introduction
Background
The number of older adults worldwide (ie, those aged ≥65 years)
is expected to almost double over the next 30 years, from 12%
to 22% [1]. This significant aging of the population will not be
without consequences for health care systems. In fact, this
phenomenon will lead to an increase in the rate of chronic
diseases considering that the prevalence of these diseases
increases with age and that older adults are seriously affected
by them [2,3]. In this regard, healthy lifestyle habits (ie, good
nutrition, regular physical activity [PA], smoking abstinence,
the limiting alcohol consumption, and the management of stress)
could help prevent a significant number of diseases in older
adults [2], promote longevity [4], reduce frailty [5], and maintain
health [3]. For these reasons, older adults should benefit from
interventions that support their adoption of healthy lifestyles.
Moreover, with the advances in health technologies, the web is
increasingly the preferred method of intervention even among
older adults, whose internet use has been growing rapidly in
recent years [6-8]. Web-based interventions can be defined as
care or treatments that aim to promote behavior change and that
are delivered via a web browser over the internet on different
technological tools such as computers, tablets, or cell phones
[9]. Web-based interventions can take many forms, such as
educational programs, disease management programs, and
web-based group exercise programs; can include different
technologies such as artificial intelligence algorithms or
monitoring devices; and can be self-guided or human-assisted
[10]. Web-based interventions could be used to support
individuals as they adopt healthy lifestyles and would be
favorable for older adults [11,12]. In addition, such interventions
constitute an economical and accessible alternative for health
care systems [13]. In the context of a global pandemic, lifestyle
habits such as sedentary behavior and dietary changes may also
be disrupted [14], and older adults’ access to programs and
services to facilitate the adoption of healthy lifestyles, such as
gyms, can be limited [15]. As a result, web-based interventions
may represent a solution for helping older adults adopt and
maintain a healthy lifestyle [15].
In addition, the current global pandemic makes web-based
interventions all the more relevant as the modes of intervention
delivery need to be reconsidered to promote social distancing
[16,17]. The recommendations on social distancing must be
followed to preserve the population’s health, especially among
vulnerable older adults. However, despite the current need for
social distancing, we need to ensure that this mode of
intervention delivery is as suitable as in-person interventions
[16]. In particular, as older adults place a high value on trusting
relationships in behavior change, human contact needs to be
preserved through any web-based interventions that are
introduced to support their adoption of healthy lifestyles [18,19].
Among other things, human contact could be maintained by
including the support of a coach, which would also increase the
commitment of older adults to the intervention [20]. Although
the literature documents numerous web-based interventions,
their components and effects are diverse, making it difficult to
draw any conclusions about which components promote optimal
change outcomes in this population [21]. In this regard, Webb
et al [22] developed a framework to facilitate investigations of
the components of web-based interventions that will optimally
influence behavior change. They found that web-based
interventions that incorporate behavior change techniques
(BCTs) and that are theoretically grounded lead to better
outcomes in terms of health behavior change and that delivery
modes could also affect such change [22].
Although some authors have published syntheses of the literature
focused on web-based interventions designed to promote
lifestyle changes in adults [11,23], to our knowledge, no
synthesis of knowledge has focused on people aged ≥65 years.
In fact, there appears to have been only 2 systematic reviews
conducted on web-based interventions focused on healthy
lifestyle habits for people aged ≥50 years [24,25]. The primary
studies included in these systematic reviews had small sample
sizes with an average age of ≥50 years, which means that they
did not focus specifically on a population of older adults [24,25].
There is another review of the literature that examined
web-based interventions to promote PA in older adults. This
review included primary studies with samples of older adults
aged ≥55 years and other age groups as well (ie, adults) [26],
which means that the interventions included were not specific
to older adults. Although there is no consensus in the literature
on the specific age used to define old age, the World Health
Organization [2] suggests defining older adults as persons aged
≥65 years. Frequently, it is assumed that interventions
designated for young or middle-aged people will be adapted for
use with older adults [27]. However, older adults are a
heterogeneous group with multiple characteristics, and those
aged ≥65 years may have different needs to be met by the
interventions because of the biological and psychological
changes associated with aging, such as decreased functional
capacity, frailty, and changes in social position [2,28]. Moreover,
older adults should be able to benefit from accessible health
services that are adapted to their needs [29], and any
interventions developed for them must consider the challenges
associated with aging [27]. In this sense, as the components of
the interventions as well as their outcomes could differ for adults
aged ≥65 years, it is essential to explore the literature dealing
with this specific population.
The 3 syntheses of the literature that we found on web-based
interventions among people aged ≥50 or ≥55 years focused only
on quantitative studies [24-26]. However, the literature also
provides qualitative studies on this subject, and they can make
relevant contributions to the components and outcomes of
web-based interventions designed to promote healthy lifestyles
among older adults. For example, qualitative studies may
provide more information about the experiences of older adults
who participate in web-based interventions, particularly with
regard to the components of the intervention, which is important
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for the development of knowledge in this area and for future
studies. A scoping review on this topic would appear appropriate
as it will permit an exploration of the available literature by
including both qualitative and quantitative studies. To our
knowledge, no study has explored the extent of knowledge of
web-based interventions for people aged ≥65 years by including
both qualitative and quantitative studies.
Objectives
Therefore, the purpose of this study is to explore the extent of
the literature on web-based interventions aimed at promoting
healthy lifestyles among people aged ≥65 years.
Methods
Overview
A scoping review was conducted based on the framework
proposed by Levac et al [30]. According to Levac et al [30], a
scoping review may be conducted to determine the scope of the
research or map the available literature on a phenomenon, which
is the purpose of this review. This review followed the 5 steps
of the framework developed by Levac et al [30], as presented
in the following sections. The protocol for this scoping review
is available elsewhere [31].
Identifying the Research Questions
The research questions were identified following a brief review
of the initial literature and discussions with the research team,
which was composed of a doctoral student (AL), a researcher
(VD), and a librarian (RB). This scoping review will seek to
answer the following questions: (1) What are the web-based
interventions aimed at promoting healthy lifestyles among
people aged ≥65 years? (2) What are the components of these
interventions (ie, use parameters, BCTs, delivery modes, and
theories used)? (3) What are the reported outcomes of these
interventions?
Identifying Relevant Studies
To identify relevant studies, the following databases were
consulted: MEDLINE, CINAHL, PsycINFO, Web of Science,
the Cochrane Database of Systematic Reviews, and the Joanna
Briggs Library. These databases were selected for their focus
on the social and health sciences, the field related to the topic
of this study. Gray literature was searched using the Google
Scholar and OpenGrey databases. The reference lists of the
identified articles were checked to ensure that all the relevant
articles had been included. The authors of the primary studies
were contacted when additional information was required.
The search strategy used keywords and descriptors related to
the concepts of older adults, lifestyle, and web-based
interventions. The complete search strategies for each database
are presented in Multimedia Appendix 1. The criteria for
inclusion were (1) articles published between 1990 and 2021
as the World Wide Web was created in 1989 [32]; (2) articles
published in French or English; (3) articles related to the
objective of the scoping review, that is, a web-based intervention
delivered via a web browser over the internet addressed to a
population of older adults and aimed at promoting healthy
lifestyle habits (ie, diet, regular PA, smoking abstention, limiting
the alcohol consumption, and management of stress); and (4)
primary studies such as experimental studies, quasi-experimental
and qualitative studies, systematic reviews, and other documents
associated with gray literature (such as government reports and
clinical practice guidelines). Research protocols were also
included as they often provide a more in-depth description of
the intervention studied. For the purpose of this study,
web-based interventions were defined as care or treatments
aimed at changing behavior and accessed via a web browser
over the internet [9]. The scope excluded teleconsultations with
health care professionals and websites that provided information
without any interaction. Articles were excluded when (1) persons
aged ≥65 years were not the population specifically studied; (2)
the web component of the intervention was not predominant,
such as a face-to-face intervention that was complemented by
a web-based component; and (3) healthy lifestyle habits were
not primarily targeted by the intervention, such as symptom
self-management programs that included some physical exercise.
Finally, the identified articles were exported to a data
management software program (ie, Covidence) where duplicates
were removed. The final database search was performed on
June 23, 2021.
Study Selection
The studies were selected by 2 independent persons (AL and
ML). An initial selection was made by reading the abstracts and
titles of the articles, and then the selected articles were read in
full, retaining only those related to the purpose of the study and
the research questions and that met the established inclusion
and exclusion criteria. In cases where there was disagreement
over a selection, a third person (VD) was consulted. As
suggested by Levac et al [30], the 2 persons who selected the
studies met at the beginning, midpoint, and end of the selection
process to clarify any difficulties they had encountered and
revise the research strategy. Inclusion and exclusion criteria
were clarified in terms of the definition of the web-based
interventions (ie, delivered via a web browser over the internet
regardless of the technological tool used, such as a tablet or a
computer) to be included as well as the population (ie, excluding
studies that targeted multiple age groups) and the behavior
targeted (ie, excluding web-based interventions for fall
prevention that included some exercises). To promote
transparency, a PRISMA (Preferred Reporting Items for
Systematic Reviews and Meta-Analyses) diagram was used to
illustrate the study selection process and present the excluded
articles and the reasons for their exclusion.
Charting the Data
Data were extracted into a table including the authors, year and
location of publication, purpose, type of study, population and
sample, method, intervention and comparison, intervention
components, and outcomes. As suggested by Levac et al [30],
data from the first 5 papers were extracted independently by 2
persons (AL and ML) to ensure compliance. As the purpose of
the review was to explore the breadth of knowledge rather than
assess the rigor of the studies identified, the quality of the studies
was not assessed.
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Collating, Summarizing, and Reporting the Results
To collect, synthesize, and report the results, we used a
conceptualization of web-based intervention components
proposed by Webb et al [22]. As mentioned previously, this
conceptualization seeks to classify the components of web-based
interventions into 3 categories: the BCTs used, the delivery
modes, and the theories used. Webb et al [22] conceived this
framework based on the BCT taxonomy developed by Michie
et al [21], on a coding scheme for classifying delivery modes,
and on the coding theory scheme by Michie and Prestwich [33].
In its most recent version, the taxonomy by Michie et al [21]
details 93 BCTs as strategies used in interventions to promote
behavior change, including feedback, action planning, and
instruction, among others. The coding scheme for the delivery
modes includes additional modes for the web component. It can
be used to categorize them as automated functions (eg, video,
automated tailored feedback, and automated following messages
such as reminders or encouragement), communicative functions
(eg, chat session, peer-to-peer access, and “ask the expert
facility”) and additional modes (eg, email, phone calls, and
videoconferencing) [22]. The Michie and Prestwich [33] coding
scheme contains questions to assess whether and how theories
are used in an intervention. Synthesizing the components of the
web-based interventions into these categories (ie, BCTs, delivery
modes, and theories) facilitates an understanding of the
components that can have an influence on health behavior
change. We also used additional items in the
CONSORT-EHEALTH (Consolidated Standards of Reporting
Trials of Electronic and Mobile Health Applications and Online
Telehealth) guidelines proposed by Eysenbach [9] for reporting
eHealth trials. This guideline proposes including use parameters
such as the number of sessions, duration of the intervention,
and frequency when reporting web-based intervention
components.
Analysis
To summarize the data by examining the components and
outcomes of the interventions studied, we drew on the method
of thematic analysis used by Paillé and Mucchielli [34] to
analyze the data. More specifically, components of the
web-based interventions were grouped according to their
similarity, divergence, complementarity, or recurrence. Themes
were identified from the extracted data based on the results of
the interventions. Again, these themes were grouped into
thematic clusters (ie, groups of themes with common
characteristics). For example, the themes “tailored content to
each participant” and “personalized advice in regard to
preference and condition” could be grouped together into the
thematic cluster “tailoring.” The analysis was carried out by the
first author (AL) and then validated by the second author (VD).
The PRISMA-ScR (Preferred Reporting Items for Systematic
Reviews and Meta-Analyses extension for Scoping Reviews)
checklist [35] was used to ensure that all the key items were
reported and promote study replicability (Multimedia Appendix
2). A narrative summary is presented in the next section as an
overview of the components and outcomes of the interventions
studied.
Results
Search Results
Initially, 12,940 articles were identified. After removing
duplicates, 10,599 articles were filtered, and 10,540 (99.44%)
of these were excluded by reading the article titles and abstracts
based on the inclusion and exclusion criteria. Finally, 0.56%
(59/10,599) of the articles were read in their entirety. A total of
20 articles were included in this review. The main reason for
excluding an article was when it had a study population that
was not specifically older adults aged ≥65 years. The PRISMA
diagram shown in Figure 1 summarizes the process used to
identify and select the studies.
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Figure 1. PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram of the literature search and article selection
process.
What Are the Web-Based Interventions Aimed at
Promoting Healthy Lifestyles Among People Aged ≥65
Years?
Among the 20 articles included, we found studies focused on
11 distinct interventions: Active for Life (n=1, 5%), Active Plus
(n=2, 10%), Active Plus 65 (n=2, 10%), eMIND (n=2, 10%),
Healthy Ageing Supported by Internet and Community (n=1,
5%), Health Aging Through Internet Counseling in the Elderly
(n=6, 30%), Life Project (n=1, 5%), MyPlan 2.0 (n=1, 5%), and
Otago (n=2, 10%), plus 2 other interventions not named [36,37].
The range of publication years was 2013 to 2021, and most of
the studies (19/20, 95%) were published in the last 5 years.
These studies were mainly published in the Netherlands (9/20,
45%), France (3/20, 15%), Australia (1/20, 5%), Italy (2/20,
10%), Switzerland (1/20, 5%), Spain (1/20, 5%), Belgium (1/20,
5%), Finland (1/20, 5%), and the United States (1/20, 5%). In
total, of the 20 studies, there were 6 (30%) randomized
controlled trials, 4 (20%) research protocols, 5 (25%) qualitative
studies, 3 (15%) pretest-posttest studies, 1 (5%) randomized
pilot study, and 1 (5%) mixed methods study. The sample sizes
identified varied from 16 to 2624 participants. The interventions
targeted older adults aged ≥65 years living in the community
[38] who could walk without technical help (2/20, 10%) [39-41],
were prefrail (1/20, 5%) [42], were inactive (3/20, 15%)
[36,37,43], had a chronic disease with a disability (2/20, 10%)
[44-47], presented 2 or more cardiovascular risk factors or an
antecedent of cardiovascular disease (1/20, 5%)
[12,19,20,48-50], or were at risk of cognitive decline (1/20, 5%)
[51,52]. The average age of the study samples ranged from 68.7
to 83 years, with an average age of 73 years, which means that
the studies targeted younger older adults. Indeed, only 20%
(4/20) of the studies (3/11, 27% of the interventions) had a
sample with an average age of ≥75 years [36,42,46,47].
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Most of the articles (9/20, 45%) were focused on evaluating the
effects of a web-based intervention on the health behaviors of
older adults, including PA and diet, or on other variables,
including cardiovascular risk factors such as diabetes, obesity,
hypertension, and hypercholesterolemia; self-efficacy [12];
knowledge and skills [38]; and cognitive function [12,51]. Other
articles presented a description of the intervention (6/20, 30%)
or the experience of the older adults with regard to their
participation in the intervention (5/20, 25%), such as the
appreciated component, reason for participation, or preferences.
All the interventions (11/11, 100%) targeted PA, and 73% (8/11)
of them targeted PA as the only behavior. In total, 18% (2/11)
of the interventions targeted PA in addition to other behaviors
such as nutrition [51,52] and nutrition and alcohol consumption
[38]. A total of 9% (1/11) of the interventions targeted all the
cardiovascular risk factors such as PA, blood pressure, diabetes,
weight, nutrition, and smoking cessation, and participants could
choose their health priorities [12,19,20,48-50]. A summary table
of the study characteristics is available in Multimedia Appendix
3[12,19,20,36-52]. The components of the interventions (ie,
use parameters, BCTs, delivery modes, and theories) are
presented in the following sections.
What Are the Components of These Interventions?
Use Parameters
Use parameters refer to the duration of the intervention, the
duration of each session, the number of sessions, and their
frequency. In this review, the duration of the intervention varied
from 5 weeks to 18 months. Only 18% (2/11) of the
interventions reported the duration of each session [37,39,40].
It would have been helpful to know the duration of each session
in the other studies to understand how much time older adults
need to spend to complete the sessions. The number of sessions
varied from 5 (1/11, 9%) [41] to 16 (1/11, 9%) [39,40]. Although
the total number of sessions was not specified, in some
interventions, the authors proposed an intensity of intervention
such as 5-minute daily sessions (1/11, 9%) [37] or free access
during the entire study period (2/11, 18%) [36,51,52]. Otherwise,
in 45% (5/11) of the interventions, the number of sessions was
not reported [12,19,20,38,42,44-50], which does not allow us
to understand how many web-based intervention sessions older
adults require. The session frequency was daily [37], twice per
week [39,40], weekly [41], and once every 2 weeks [43]. In
45% (5/11) of the interventions, the frequency of sessions was
not reported [12,19,20,38,42,44-50]. These were the same
studies that did not report the number of sessions.
BCTs Component
BCTs are the strategies used in interventions to promote
behavior change, such as feedback, action planning, or
instruction [21]. On the basis of the taxonomy by Michie et al
[21] comprising 93 BCTs, we identified 15 different BCTs used
in the web-based interventions. The number of BCTs used varied
from 1 to 9, with an average of 4.5, meaning that all the
interventions except 1 (10/11, 91%) [42] combined multiple
BCTs. As the combination of BCTs was varied, it is difficult
to establish links between the most used or effective
combinations and the results obtained and discern the
contribution made by each of them. However, we identified a
trend in the combination of instruction, self-monitoring, and
feedback (7/11, 64%) despite the fact that this combination was
mostly coupled with one or more other BCTs such as action
planning or goal setting.
All the interventions (11/11, 100%) used instructions on how
to perform the behavior. Most of the interventions included
feedback on the targeted health behavior (9/11, 82%)
[12,19,20,36,37,39-41,43-45,48-50] and self-monitoring of the
behavior (8/11, 73%) [12,19,20,36-41,43,48-52]. Other BCTs
used included action planning (5/11, 45%) [36,41,43-47], goal
setting (4/11, 36%) [12,19,20,37,41,43,48-50], problem solving
(4/11, 36%) [12,19,20,37,41,46-50], awareness (1/11, 9%)
[46,47], verbal persuasion (1/11, 9%) [39,40], commitment
(1/11, 9%), self-regulation (1/11, 9%) [44,45], prompts and
cues (1/11, 9%), rewards (1/11, 9%), social comparison (1/11,
9%), and relapse prevention (1/11, 9%) [43]. Although common
techniques were identified, they were rarely explained by the
authors, which makes their definition and application unclear.
In fact, only 18% (2/11) of the interventions provided detailed
discussions on how the BCTs were used to generate the desired
change [41,43]. For example, Alley et al [43] detailed that action
planning was used by asking questions about the participants’
actions in terms of what, when, and where to perform the
behavior. Several other BCTs from the taxonomy by Michie et
al [21], such as distraction, self-affirmation, or scheduled
consequences, were not identified in this review. For a summary
of the BCTs used in each web-based intervention, see
Multimedia Appendix 4 [12,19,20,36-52].
Delivery Modes
The delivery modes include every mode in addition to the web
component grouped as follows: automated function (eg, video,
automated tailored feedback, and automated following messages
such as reminders or encouragement), communicative function
(eg, chat session, peer-to-peer access, and “ask the expert
facility”), and additional modes (eg, email, phone calls, and
videoconferencing).
Automated Function
All interventions (11/11, 100%) included behavioral information
such as how to stay active, guidelines on PA, a workout plan,
and safety when exercising. Some interventions included videos
on how to modify behavior (4/11, 36%)
[12,19,20,36,38,42,48,49], an electronic diary to track behavior
(2/11, 18%) [12,19,20,36,38,42,48,49], a quiz (1/11, 9%) [41],
and reminders to use the platform either sent by email (2/11,
18%) [41,43] or provided throughout the platform (1/11, 9%)
[12,19,20,48-50]. Many interventions (5/11, 45%) offered
automated, tailored feedback based on individual progress either
throughout the platform [37,41,43] or by email [44-47]. Among
these, in the case of 9% (1/11) of the interventions, a
combination of automated and personal feedback was provided
[12,19,20,48-50].
Communicative Functions
A total of 27% (3/11) of the interventions included a messaging
system that offered the possibility of chats with a coach
[12,19,20,39,40,48,49,51,52], and 18% (2/11) proposed a chat
forum with peers [38-40]. In 9% (1/11) of the interventions,
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participants received written feedback from a physiotherapist
[36], but it remains unclear whether this was automated or
personal. Some interventions (2/11, 18%) also included
in-person meetings, such as an initial meeting with the coach
[12,19,20,36,48,49] and a monthly peer mentor meeting [36].
Other interventions included the possibility of training with
peers on the web (1/11, 9%) [39,40] and receiving phone calls
from a member of the research team (1/11, 9%) [36]. Some
interventions (4/11, 36%) also offered participants an
opportunity to take part in local group activities
[12,19,20,42,44-49], but the authors provided no information
on how many participants took part in these activities.
Supplementary Modes
The studies included in this scoping review were focused on a
web-based intervention but, as discussed previously, some of
them also included a supplementary delivery mode such as
email (3/11, 27%) [41,44-47], phone calls (2/11, 18%)
[10,17,18,36,48-50], and face-to-face contact (2/11, 18%)
[12,19,20,36,48,49]. No intervention used SMS text messaging
or videoconferencing. However, as most of the studies that
included supplementary modes (7/8, 88%) did not provide
information on the impacts of these additional modes on
behavior change in older adults, it is difficult to know how such
modes influenced the results.
Theory
The coding scheme developed by Michie and Prestwich [33]
allows for an assessment of the extent to which the interventions
are theory-based. In total, 55% (6/11) of the interventions were
based on at least one theory. A total of 18% (2/11) of the
interventions were based on 1 theory, whereas 36% (4/11) were
based on 2 to 5 theories. The theories used were the theory of
planned behavior (2/11, 18%), social cognitive theory (2/11,
18%), precaution adoption process (1/11, 9%), integrated model
for change (1/11, 9%), self-regulation theory (3/11, 27%),
transtheoretical model (2/11, 18%), self-determination theory
(2/11, 18%), motivational interviewing (1/11, 9%), I-Change
Model (1/11, 9%), and health action process approach (1/11,
9%).
Among the theory-based interventions, most of the studies (5/6,
83%) did not explicitly state how the theory was used to develop
the intervention or how it was integrated into the intervention
to lead to the desired change. Therefore, it is difficult to
understand the contribution made by the theories used and link
them to the results obtained. This was despite the fact that the
authors of the papers on all the theory-based interventions
specified that they wanted to act on constructs of the theory
related to the targeted behavior change, such as self-efficacy,
motivation, or attitudes, yet they did not provide definitions of
the constructs or explanations of how they operationalized them.
Definitions and explanations of the constructs would be needed
to understand how the intervention attempted to act on them
and lead to behavior change [53]. In only 18% (2/11) of the
interventions [41,43] did the authors link theory constructs with
the BCTs used. For example, Alley et al [43] indicated that the
BCTs instruction and feedback were used to change the attitudes
of the participants. In other studies (9/11, 82%), no connection
was made between the BCTs and theory constructs when this
would have helped us understand how the chosen BCTs would
lead to the desired change in terms of the constructs targeted.
Otherwise, the authors of the papers on 18% (2/11) of the
interventions measured a construct of the theory, such as
self-efficacy, as an outcome of the study [12,43]. The authors
of the papers on 9% (1/11) of the interventions specified that
the theory’s constructs were used to tailor the intervention to
the participants such that the content of the advice depended on
the intrinsic motivation of the participant [44,45]. Table 1
presents a summary of the components of the interventions
surveyed.
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Table 1. Summary of the intervention components (N=11).
TheoryBehavior change tech-
nique
Delivery modeUse parametersPopulation and behav-
ior
Intervention
Active for Life
[43]•Theory of planned
behavior
•Instruction
•Automated func-
tion: tailored feed-
back on PA via the
•Duration of the inter-
vention: 12 weeks
•Older adults aged
≥65 years who did
not meet the rec- •Goal setting
•Duration of each
session: NRb•Social cognitive
theory
•Self-monitoring
platform based on
the participants’
ommendations for
PAa•Action planning
•Prompts and cues
•Number of sessions:
6characteristics •Rewards and re-
lapse prevention
•PA •Communicative
function: none
•Frequency: bimonth-
ly •Social comparison
•Additional modes:
none •Feedback
Active Plus
[44,45]•Theory of planned
behavior
•Instruction
•Automated func-
tion: tailored advice
on PA and feedback
•Duration of the inter-
vention: 4 months
•Older adults aged
≥65 years who had
at least one chron- •Action planning
•Duration of each
session: NR •Precaution adop-
tion process
•Coping planning
via emailic disease that af- •Commitment
fects mobility and •Communicative
function: none
• •
Number of sessions:
NR Integrated model
for change
•Self-regulation
were able to walk •Feedback
100 m without •Additional modes:
list of local group
• •
Frequency: NR Self-regulation
modelhelp activities
•PA
Active Plus 65
[46,47]•I-Change Model
•Instruction
•Automated func-
tion: tailored advice
on PA via email
•Duration of the inter-
vention: 4 months
•Older adults aged
≥65 years with an
impairment in PA •• Transtheoretical
model
Problem solving
•Duration of each
session: NR •Action planning
caused by a non- •Communicative
function: none •Self-determination
theory
•Coping planning
communicable
chronic disease •Number of sessions:
NR •Awareness
•Additional modes:
list of local group •Self-regulation
theory
•Feedback
•Frequency: NR
•PA activities and email •Health action pro-
cess approach
eMind [51,52]•NR
•Instruction
•Automated func-
tion: tailored exer-
•Duration of the inter-
vention: 6 months
•Community-
dwelling older •Feedback
cise program, nontai-adults aged ≥65 •Duration of each
session: NR •Self-monitoring
with activity tracker
lored nutritional ad-
vice, and website
years who present-
ed a subjective •Number of sessions:
NR link to a cognitivememory complaint
without dementia training
•Frequency: free ac-
cess
•PA and nutrition •Communicative
function: chat with
health professionals
anytime and chat
with a dietician for
people at risk of nu-
tritional deficiency
•Additional modes:
none
Healthy Ageing
Supported by •NR
•Instruction and self-
monitoring
•Automated func-
tion: information on
•Duration of the inter-
vention: 10 weeks
•Older adults aged
≥65 years physical (food andInternet and •Duration of each
session: NR
•PA, nutrition, alco-
hol consumption,Community
[38]drink), social (pre-
venting loneliness),
and emotional (eg,
and social partici-
pation •Number of sessions:
NR self-esteem and re-
•Frequency: NR silience) health and
videos
•Communicative
function: chat forum
•Additional modes:
none
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TheoryBehavior change tech-
nique
Delivery modeUse parametersPopulation and behav-
ior
Intervention
•Motivational inter-
viewing
•Transtheoretical
model
•Social cognitive
theory
•Instruction
•Goal setting
•Self-monitoring
•Problem solving
•Automated and per-
sonal feedback
•Automated func-
tion: tailored
lifestyle and cardio-
vascular feedback,
electronic diary, ed-
ucational content,
and peer-to-peer
videos
•Communicative
function: personal
and automated feed-
back from a coach
with the possibility
to chat
•Additional modes:
12-month phone
call and list of local
group activities
•Duration of the inter-
vention: 18 months
•Duration of each
session: NR
•Number of sessions:
NR
•Frequency: NR
•Older adults aged
≥65 years with
high cardiovascu-
lar risk
•Smoking, blood
pressure, choles-
terol, diabetes,
weight, PA, and
nutrition
HATICEc
[12,19,20,48-50]
•Self-determination
theory
•Instruction
•Automated func-
tion: healthy
lifestyle and PA in-
formation, exercise
videos, and the pos-
sibility to create a
tailored program
•Communicative
function: none
•Additional modes:
list of local group
activities
•Duration of the inter-
vention: NR
•Duration of each
session: NR
•Number of sessions:
NR
•Frequency: NR
•Prefrail older
adults aged 74 to
91 years
•PA
Life Project
[42]
•Self-regulation
theory
•Instruction
•Computer-tailored
feedback
•Goal setting
•Problem solving
•Action planning
•Self-monitoring
•Automated func-
tion: information
about PA, quiz
about PA and bene-
fits, and tailored
feedback
•Communicative
function: none
•Additional modes:
email reminders
•Duration of the inter-
vention: 5 weeks
•Duration of each
session: NR
•Number of sessions:
5
•Frequency: each
week and free ac-
cess
•Older adults aged
65 to 80 years able
to walk 100 m
without help
•PA
MyPlan 2.0
[41]
•NR
•Instruction
•Feedback
•Self-monitoring
•Verbal persuasion
•Automated func-
tion: exercise in-
struction and plan
•Communicative
function: possibility
to communicate
with a coach and
peers
•Additional modes:
possibility to train
with peers on the
web
•Duration of the inter-
vention: 8 weeks
•Duration of each
session: 30 to 40
minutes
•Number of sessions:
16
•Frequency: 2 times
per week
•Community-
dwelling older
adults aged ≥65
years who were
not frail
•PA
Otago [39,40]
•NR
•Instruction
•Self-monitoring
•Feedback
•Goal setting
•Problem solving
•Duration of the inter-
vention: 2 months
•Duration of each
session: 5 minutes
•Number of sessions:
NR
•Frequency: every
day
•Inactive older
adults aged ≥65
years
•PA
No name [37]
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TheoryBehavior change tech-
nique
Delivery modeUse parametersPopulation and behav-
ior
Intervention
•Automated func-
tion: exercise in-
struction and exam-
ples through an em-
bodied conversation-
al agent
•Communicative
function: none
•Additional modes:
implementation of
the intervention in a
clinic waiting room
for 12 months
•NR
•Instruction
•Action planning
•Self-monitoring
•Feedback from a
therapist
•Automated func-
tion: tailored PA in-
formation, exercise
video, and diary
•Communicative
function: feedback
from a physiothera-
pist, peer mentor
meeting once a
month, phone calls
from a researcher
after 2 to 3 weeks,
and optional phone
support
•Additional modes:
first meeting in
group, phone call,
and face-to-face
meeting
•Duration of the inter-
vention: NR
•Duration of each
session: NR
•Number of sessions:
NR
•Frequency: NR
•Older adults aged
≥70 years with
self-reported im-
paired balance,
able to rise from a
high chair and
stand without sup-
port, and not ac-
tive
•PA
No name [36]
aPA: physical activity.
bNR: not reported.
cHATICE: Healthy Ageing Through Internet Counselling in the Elderly.
What Are the Reported Outcomes of These
Interventions?
Outcomes
Of the 6 studies that evaluated the effects of web-based
interventions on PA in older adults, 4 (67%) found positive
outcomes on PA after the intervention, including increasing
weekly minutes of moderate to vigorous PA and greater
likelihood of performing self-reported cycling [37,41,45,46].
Other positive effects were also reported on blood pressure,
lipid levels, BMI, smoking cessation, self-efficacy [12], and
participants’ knowledge and skills to adopt a healthy lifestyle
[38]. Among the 35% (7/20) of studies that conducted a
qualitative evaluation, 5 themes emerged from the thematic
analysis, which are detailed in the following sections: tailoring,
motivation, support, barriers, and perceptions.
Theme 1: Tailoring
Several studies (4/7, 57%) addressed the concept of tailored
web-based interventions. Indeed, older adults mentioned that
they appreciated participating in a web-based intervention
tailored to their limitations and preferences [20,42]. Some
participants mentioned that the exercises proposed in the
web-based intervention were too easy and repetitive, not adapted
to their environment [51], or of limited value owing to their
medical condition [36]. This suggests the need for web-based
interventions tailored to older adults’preferences, environments,
and conditions.
Theme 2: Motivation
Motivation appeared to be central to behavior change among
older adults as most studies (6/7, 86%) addressed it. Participants
stated that web-based interventions should help increase their
motivation for change [19,51]. Identifying their own motivation
is important for older adults to maintain behavior change [36],
and the sources of such motivation varied, including personal
benefits and health improvement [19,36]. Some participants
mentioned that the coach’s positive message could help boost
their motivation [20,51] and that being motivated helped them
continue using the intervention and vice versa [20]. Other
participants argued that behavior change, such as being more
physically active, was not a goal in itself but rather that they
were motivated by other health benefits such as remaining
independent as long as possible [42]. For these reasons, it would
appear necessary to explore the individual motivations of each
older adult to facilitate change.
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Theme 3: Support
The theme of support was reported in several studies (5/7, 71%).
Older adults mentioned that they need support to achieve their
health goals [19] and that the platform could provide continuous
support [20], especially as training alone at home requires
discipline [42]. Some participants mentioned that they
appreciated having discussions with a coach throughout the
web-based intervention [19,40]. Some participants also argued
that a first meeting with the coach was necessary to develop a
relationship of trust and then facilitate change and that the coach
played an important role in stimulating the initial use of the
web-based intervention and sustaining it [20]. In other words,
participants who feel connected with the coach are more likely
to keep using the platform and continue pursuing goals for
lifestyle changes [20]. In addition, participants in the study by
de Souto Barreto et al [51] would have appreciated having more
contact with a member of the research team, suggesting that, as
older adults, they would have been favorable to having the
support of a coach. Conversely, peer interactions were less
valued and used by older adults [40].
Theme 4: Barriers
Some barriers were consistently identified in the studies (4/7,
57%) on the use of web-based interventions among older adults.
First, older adults mentioned barriers to the web-based
interventions, such as a lack of computer skills [19,20] or using
an old computer [42]. Difficulties encountered in computer use
or limited internet skills could discourage older adults from
using a web-based intervention [20]. Some participants also
mentioned that they sometimes lacked the discipline required
to exercise alone at home [36,42]. Without the support of a
coach, older adults feared getting hurt [42], which could be a
barrier to behavior change.
Theme 5: Perceptions
Behavior change among older adults seemed to be influenced
by their perceptions of their age and the benefits of change.
Indeed, older adults who do not perceive a need to improve their
lifestyles or who do not prioritize it because of their advanced
age are less likely to use a web-based intervention and, therefore,
engage in lifestyle changes [20]. By contrast, perceiving that
behavior change could lead to health benefits positively
influences older adults toward using the intervention [19,20].
However, this theme emerged in only 29% (2/7) of the studies,
which is why it may have had less impact than the other themes.
Discussion
Principal Findings
Overview
This scoping review sought to explore the extent of the available
literature on web-based interventions as a way to promote
healthy lifestyles among people aged ≥65 years. In total, 11
different interventions discussed in 20 published articles were
included in this review. Almost all the articles (19/20, 95%)
were published in the last 5 years, which indicates growth in
the development and evaluation of this type of intervention
among older people. This is consistent with the increased use
of the web by older adults in recent years [6-8] and the urgent
need to deploy cost-effective strategies to facilitate access to
health care [13-15].
As found in other studies [22,54], our results show that the
studies included predominantly young older adults, with few
that took an interest in the “oldest old” such as persons aged
≥85 years. As the literature is so limited on web-based
interventions involving people of more advanced age (eg, ≥85
years) and as the components and effects of web-based
interventions may differ for this population, further studies are
needed across the aging spectrum. This scoping review found
that web-based interventions among older adults are mainly
focused on increasing PA. This high prevalence could be
explained by the fact that older adults are considered the most
sedentary age group [55] and that the benefits of PA are
considerable for this population [56]. Given that other lifestyle
habits such as diet, stress, and alcohol consumption [2] would
also be favorable to the health of older adults, more studies
should be conducted to evaluate the effects of web-based
interventions on these habits in this population.
Components
The web-based interventions included in this review had various
components. The interventions were diverse in terms of their
use parameters (ie, duration, number of sessions, completion
time, and frequency). In almost all the interventions (10/11,
91%), at least one detail regarding the use parameters was
omitted, making it difficult to understand the intensity of the
interventions offered to older adults. As noted in the studies
examining preferences toward web-based interventions, older
adults prefer a few 30-minute sessions [57] or shorter 10-minute
sessions on a regular basis every 2 or 3 days [57,58]. Among
adults, other studies have shown that web-based interventions
that are more intensive [24], that allow for longer durations,
such as 60-minute sessions or more, and that propose a total
number of sessions of >3 [59] are more effective at producing
behavior change [24,59]. In this review, because of a lack of
detailed information on use parameters, it was difficult to
identify any trend in use parameters that were more relevant to
supporting change among older adults. Further research is
needed to better investigate the optimal use parameters (ie,
duration, number of sessions, completion time, and frequency)
of web-based interventions for older adults.
In this review, we found that the most used BCTs were
instruction, feedback, and self-monitoring, which is similar to
the findings of other studies that explored the use of BCTs in
web-based interventions [22,60]. However, BCTs were used in
combination without explaining why these choices were made
and how they were operationalized. For this reason, it was
difficult to discern the contribution of each BCT to the results
obtained [61] and how they could have led to change [53]. BCTs
are the active ingredients in an intervention to effect behavior
change. Therefore, it is crucial for authors to be explicit about
their choice of BCT combinations to understand how
interventions produce their effects [62]. Although some
interventions (2/11, 18%) observed that some BCTs such as
self-regulation could be effective for adults and not for older
adults [63,64], further research is needed to understand which
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BCTs are more appropriate to support older adults in their
adoption of healthy lifestyles. In particular, further studies are
needed to explore which combinations of BCTs could optimize
the intervention’s impact and how each BCT interacts with the
others within an intervention to produce behavior change among
older adults [62].
The results of this review show the diverse range of delivery
modes used in web-based interventions. Some included an
electronic diary (2/11, 18%), quiz (1/11, 9%), or videos (4/11,
36%) as well as supplementary modes such as phone calls (2/11,
18%), face-to-face meetings (2/11, 18%), and email reminders
(3/11, 27%). All the interventions (11/11, 100%) provided
instructions on how to perform the behavior, which other authors
have pointed out as the core of most web-based interventions
[65]. Many (5/11, 45%) proposed automated feedback, which
would be one of the most effective delivery modes leading to
behavior change in adults [26]. Only 9% (1/11) of the
interventions included a forum with peers, and it was underused
by participants, which is inconsistent with other studies in which
adults aged ≥50 years showed high use [66] and appreciation
[67]. Few interventions (3/11, 27%) offered a chat with a coach,
which is similar to the findings of other studies on web-based
interventions among adults [22,59]. In the interventions that
did offer a chat with a coach, participants were offered an
opportunity to communicate with a coach if needed rather than
for constant support. The actual nature, dose, and type of
coaching provided by this coach was poorly reported by the
studies. However, as pointed out by other authors, the constant
support of a coach throughout a web-based intervention could
replace the sense of interpersonal connectedness found in
in-person interventions [68], which older adults seek [18,69].
A systematic review also found that web-based interventions
that include human support are more effective at behavior
change among middle-aged and older adults than stand-alone
interventions [24]. That being said, although a certain delivery
mode could be more appropriate for older adults [27], the
literature on this subject is limited. More studies, such as
meta-analyses, are needed to identify which delivery modes are
more effective at inducing behavior change among older adults.
This could guide the design of future interventions. Further
studies should also investigate web-based interventions that
give older adults the choice to participate in a group forum, as
well as different forms of coaching by a professional throughout
the web-based interventions. More studies are needed to examine
the nature of the role played by the coach throughout a
web-based intervention as well as explore the dosing and type
of support needed to help older adults adopt healthy lifestyles.
For us, it seems clear that this coaching could be provided by
a health professional such as a nurse as it is a nurse’s role to
support people in health promotion and older adults appreciate
developing a trusting relationship with a nurse [69].
In this review, few of the interventions (6/11, 55%) were based
on a theory. The most common theory reported was the
self-regulation theory. This differs from other reviews, in which
one mainly finds the social cognitive theory in behavior change
interventions among middle-aged [70] and older adults [71].
Among the theory-based interventions, most of the studies (5/6,
83%) did not report how the theory was used to design an
intervention that would lead to the desired change. For this
reason, it is difficult to draw conclusions regarding the theories
and the results obtained. Theories help explain why and how
behavior change occurs and provide guidance on the potential
determinants to be targeted by the intervention to induce
behavior change [62]. In addition, designing interventions based
on theories allows us to link the theoretical determinants of
behavior change with intervention components [33], know which
BCTs to use [62], and ensure that the intervention will lead to
behavior change. Indeed, it is well known that theory-based
interventions are more effective than non–theory-based
interventions [72], and this has also been demonstrated in a
population of older adults [71]. Future web-based interventions
to promote healthy lifestyles among older adults should be based
on theory, and researchers should clearly state how theories
guide the development of their interventions. Further studies
are needed to compare interventions based on different theories
in terms of the effects identified on the lifestyles of older adults.
Outcomes
As reported in previous studies [65,70], we observed a favorable
trend in the use of web-based interventions to increase PA
among older adults. This review found that web-based
interventions can also have positive effects on blood pressure,
lipid levels, BMI, smoking cessation, self-efficacy and
knowledge, and the skills needed to adopt a healthy lifestyle.
This is consistent with research findings on the benefits of
web-based interventions [24,25].
As a result of our analysis, 5 themes emerged that appear to be
central to web-based lifestyle change interventions among older
adults: tailoring, motivation, support, barriers, and perceptions.
As has been pointed out by many authors [29,73-75], the results
of this review show that motivation is one of the most important
factors influencing the lifestyle habits of older adults. As
motivation is an intrinsic factor (ie, each person must identify
their own), increasing individual motivation among older adults
may facilitate behavior change [76]. In this sense, future
web-based interventions among older adults should target this
determinant as a way to help them adopt healthy lifestyle habits
[26]. This review also found that older adults appear to
appreciate interventions that include support from a coach,
which also supports their motivation for change and engagement
with the intervention. These findings are consistent with other
studies in which older adults mentioned that support and the
development of a relationship of trust are necessary in behavior
change interventions [18,29,77]. This finding may inform the
development of future web-based interventions intended to
promote healthy lifestyles among older adults by including the
support of a coach.
In line with the results of other studies [57,78], this review
highlighted the fact that older adults would prefer interventions
that are tailored to their preferences and conditions. Indeed, it
appears that tailored web-based interventions can make older
adults more engaged in behavior change [79] and lead to better
recall of information [80]. Previous studies have suggested that
tailored web-based interventions are more effective at inducing
behavior change than generic interventions in a middle-aged
adult population [25,26]. For older adults, designing a tailored
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web-based intervention appears to be even more important
considering the heterogeneity of this population and the various
challenges associated with aging, including comorbidities and
frailty, which are experienced differently by older adults [27].
Consistent with the findings of other authors [57,79], this review
found that older adults may face barriers to using web-based
interventions, such as lack of computer skills and difficulties
using the technology. For the development of future web-based
interventions, it would appear necessary to consider the barriers
that older adults face in using technology and find ways to
overcome them. Including access to a coach through the web
platform for initial and ongoing guidance could help reduce
such barriers and, in turn, avoid discouragement among older
adults committed to change [79]. In addition, the findings of
this review indicate that older adults’ lifestyle habits are
influenced by their perceptions of change in old age, as reported
in other studies [73,75,79]. It would appear necessary to explore
older adults’ perceptions of change and its benefits in future
studies to promote change in this population.
In summary, we believe that the results of this review provide
a better understanding of the components of web-based
interventions that can lead to behavior change among older
adults. In the studies identified, we found an overrepresentation
of interventions focused on the PA behavior of older adults and
conclude that other studies should be conducted to assess the
effects on other lifestyle habits. The results of this review lead
us to believe that authors should provide a more in-depth
description of their interventions’ components, including the
use parameters, BCTs, delivery modes, and theories used, to
understand what is favorable to the adoption of a healthy
lifestyle among other adults, how this is achieved, and how it
could have influenced participants in behavior change. In
particular, further studies should be carried out to understand
how BCTs are used in an intervention, the impact of each of
these BCTs, and the influence of the diverse delivery modes
used on behavior change among older adults. Future web-based
interventions should be based on one or more theories, and
authors should indicate how these theories are used in the
intervention to induce change. The results of this review suggest
that further studies of web-based interventions to promote a
healthy lifestyle in older adults should include support from a
coach to develop a relationship of trust, seek to increase
motivation among older adults, be tailored to older adults’
conditions, help them reduce barriers to using technology, and
modify their perceptions of effecting change at their age. We
propose that future web-based interventions be coconstructed
with older adults to better identify their needs and what they
seek, particularly with regard to support from a professional.
Limitations
Although this is not the main objective or a necessary step in a
scoping review, this review did not evaluate the quality of the
studies, which may raise concerns about the rigor of the studies
reviewed and affect the generalizability of the results. However,
we critically reviewed all the studies. In addition, a language
restriction (ie, only studies in English and French) was imposed,
and this may have affected the exhaustiveness of the set of
articles identified. In this review, we used a broad definition of
older adults (ie, aged ≥65 years [2]). These results must be
interpreted with caution given that older adults across the aging
spectrum age differently and, regardless of their age, their needs
may differ according to other characteristics such as
comorbidities and frailty. Finally, step 6 of the Levac et al [28]
framework (ie, consultation) was not completed as it was not
relevant to the objectives of this scoping review. Indeed, this
scoping review sought to explore the extent of the available
literature on web-based interventions to promote healthy
lifestyles among people aged ≥65 years, so consulting older
adults would not have provided any insight into our subject.
The consultation step may be more relevant in future studies
conducted to map the needs of older adults in web-based
interventions.
Conclusions
This study identified components and outcomes of web-based
interventions to promote healthy lifestyles among older adults.
Although a variety of components were found, this scoping
review revealed a positive trend in web-based interventions to
promote healthy lifestyles, mostly through PA. More research
is needed to further develop knowledge in this area, including
examining the oldest old, evaluating the effects on various
lifestyle habits such as diet and stress, clarifying how theories
are integrated into the intervention, and discerning the
contributions of each BCT and mode of delivery on the results
obtained. Future web-based interventions among older adults
should be coconstructed with them to ensure that the
interventions are tailored to their conditions, limitations, and
preferences; include the needed support of a coach; increase
their motivation; help them modify their perceptions of behavior
change; and reduce their barriers to using technology. Moreover,
this study did not assess the quality of the literature, so the
results must be interpreted with caution. With the current aging
of the population, the growing use of the internet by older adults
in recent years, and the pandemic context, which requires that
we review how we provide care, it remains essential to continue
developing and evaluating innovative, accessible interventions
that will promote the health of older people while meeting the
needs of an aging population. The results of this scoping review
may inform health professionals and intervention developers
about the relevant components and outcomes of web-based
interventions in a population of older adults.
Acknowledgments
This review is part of the first author’s (AL) doctoral study that is funded by the Canadian Institutes of Health Research, the
Centre Hospitalier de l’Université de Montréal Foundation, the Ordre des infirmières et infirmiers du Québec, the Quebec Network
on Nursing Intervention Research, the Université de Montréal Marguerite d’Youville Research Chair on Humanistic Nursing
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Intervention, the Université de Montréal Faculty of Nursing, and the Montreal Heart Institute Foundation. The authors would
like to acknowledge Monica Lam for her contribution to the selection of articles and data extraction.
Authors' Contributions
All the authors contributed to the design and development of this scoping review.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Search strategies.
[DOCX File , 23 KB-Multimedia Appendix 1]
Multimedia Appendix 2
PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist.
[DOCX File , 108 KB-Multimedia Appendix 2]
Multimedia Appendix 3
Characteristics of the studies.
[DOCX File , 26 KB-Multimedia Appendix 3]
Multimedia Appendix 4
Summary of the behavior change techniques used in the web-based interventions.
[DOCX File , 18 KB-Multimedia Appendix 4]
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Abbreviations
BCT: behavior change technique
CONSORT-EHEALTH: Consolidated Standards of Reporting Trials of Electronic and Mobile Health Applications
and Online Telehealth
PA: physical activity
PRISMA: Preferred Reporting Items for Systematic Reviews and Meta-Analyses
PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping
Reviews
Edited by T Leung; submitted 25.02.22; peer-reviewed by P Heyn, A Ferrario; comments to author 27.06.22; revised version received
14.07.22; accepted 06.08.22; published 23.08.22
Please cite as:
Lavoie A, Dubé V
Web-Based Interventions to Promote Healthy Lifestyles for Older Adults: Scoping Review
Interact J Med Res 2022;11(2):e37315
URL: https://www.i-jmr.org/2022/2/e37315
doi: 10.2196/37315
PMID:
©Audrey Lavoie, Véronique Dubé. Originally published in the Interactive Journal of Medical Research (https://www.i-jmr.org/),
23.08.2022. 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 the Interactive Journal of Medical Research, is properly cited. The complete
bibliographic information, a link to the original publication on https://www.i-jmr.org/, as well as this copyright and license
information must be included.
Interact J Med Res 2022 | vol. 11 | iss. 2 | e37315 | p. 18https://www.i-jmr.org/2022/2/e37315 (page number not for citation purposes)
Lavoie & DubéINTERACTIVE JOURNAL OF MEDICAL RESEARCH
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