JMIR MEDICAL INFORMATICS

Kruse et al

Review

Utilization Barriers and Medical Outcomes Commensurate With
the Use of Telehealth Among Older Adults: Systematic Review
Clemens Kruse*, MSIT, MHA, MBA, PhD; Joanna Fohn*, BS, MHA; Nakia Wilson*, BA, MHA; Evangelina Nunez
Patlan*, BS, MHA; Stephanie Zipp*, BS, MHA; Michael Mileski*, DC, MHA, MPH, MSHEd
School of Health Administration, Texas State University, San Marcos, TX, United States*

all authors contributed equally

Corresponding Author:
Clemens Kruse, MSIT, MHA, MBA, PhD
School of Health Administration
Texas State University
601 University Dr
Encino Hall 250
San Marcos, TX, 78666
United States
Phone: 1 210 355 4742
Email: scottkruse@txstate.edu

Abstract
Background: Rising telehealth capabilities and improving access to older adults can aid in improving health outcomes andquality of life indicators. Telehealth is not being used ubiquitously at present.
Objective: This review aimed to identify the barriers that prevent ubiquitous use of telehealth and the ways in which telehealthimproves health outcomes and quality of life indicators for older adults.
Methods: This systematic review was conducted and reported in accordance with the Kruse protocol and the Preferred Reporting
Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Reviewers queried the following four research databases:
Cumulative Index of Nursing and Allied Health Literature (CINAHL), PubMed (MEDLINE), Web of Science, and Embase(Science Direct). Reviewers analyzed 57 articles, performed a narrative analysis to identify themes, and identified barriers andreports of health outcomes and quality of life indicators found in the literature.
Results: Reviewers analyzed 57 studies across the following five interventions of telehealth: eHealth, mobile health (mHealth),telemonitoring, telecare (phone), and telehealth video calls, with a Cohen κ of 0.75. Reviewers identified 14 themes for barriers.
The most common of which were technical literacy (25/144 occurrences, 17%), lack of desire (19/144 occurrences, 13%), andcost (11/144 occurrences, 8%). Reviewers identified 13 medical outcomes associated with telehealth interventions. The mostcommon of which were decrease in psychological stress (21/118 occurrences, 18%), increase in autonomy (18/118 occurrences,15%), and increase in cognitive ability (11/118 occurrences, 9%). Some articles did not report medical outcomes (18/57, 32%)
and some did not report barriers (19/57, 33%).
Conclusions: The literature suggests that the elimination of barriers could increase the prevalence of telehealth use by older
adults. By increasing use of telehealth, proximity to care is no longer an issue for access, and thereby care can reach populations
with chronic conditions and mobility restrictions. Future research should be conducted on methods for personalizing telehealthin older adults before implementation.
Trial
Registration:
PROSPEROCRD42020182162;
https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020182162.
International Registered Report Identifier (IRRID): RR2-10.2196/15490(JMIR Med Inform 2020;8(8):e20359) doi: 10.2196/20359
KEYWORDS
telehealth; telemedicine; older adults; barriers; health outcomes

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Introduction
BackgroundA demographic shift has been evident globally since 2015.
Specifically, the aging population has been growing at a rapid
rate and has been predicted to reach 22% by the year 2050 [1].
In fact, the World Health Organization (WHO) estimates thatduring 2020, adults aged 60 years or older will outnumber
children aged 5 years or younger [1]. The United States Census
Bureau published a graphic on March 13, 2018, depicting the
population pyramid from 1960 and comparing it with the 2060
prediction [2]. The graphic demonstrated the gradual change of
the US population pyramid to a pillar shape [2]. This graphic
is key to understanding the demands on the health care systemin the area of geriatric, long-term, and end-of-life care, because
it highlights the larger number of older adults living longer lives.
By 2030, 60 million people in the “baby boomer” generation(born between 1946 and 1964) will have reached 65 years ofage or older and will be eligible for age-related state entitlements
in most countries [3,4]. This demographic shift is an impending
issue facing health care, as geriatric, long-term, and end-of-lifecare will experience a surge in demand. Health careorganizations and their providers must find ways to effectively
allocate resources and provide the right care at the right timeand at the right place [5].
Telemedicine has the potential to increase access among elderly
people and relieve the stress regarding care for the unusuallylarge number of elderly people. The WHO defines telemedicine
as “healing from a distance.” More specifically, it is healing
through the use of information and communication technologies“to improve patient outcomes by increasing access to care andmedical information” [6]. The WHO also does not differentiate
between the terms telemedicine and telehealth.
There has not been much work on the use of telehealth basedon age; however, we know that a technology gap or digital
divide exists. It is established by tiers of race, age, and economic
disparities [7]. In the United States, for instance, the elder-care
entitlement Medicare imposes restrictions on the use of
telehealth for the primary population [8]. The Coronavirus Aid,
Relieve, and Economic (CARES) Act provides a regulatory
waiver to extend reimbursements to telemedicine, but this is
only a relief act and not permanent legislation [9]. Previous
reviews have investigated facilitators and barriers to the adoptionof telehealth, the use of eHealth and mobile health (mHealth)tools in health promotion and primary prevention among older
adults, and patient satisfaction with telehealth interventions[10-12]. A narrative analysis on mHealth solutions for the aging
population used a generational analysis that included culture
and trust of other people and a distrust of technology [13]. This
work noted an increase in the use of technology for healthpurposes and an increase in the use of the internet for healthpurposes. It also noted concerns of security and privacy andtechnical troubleshooting. A review from 6 years ago spanned
10 years, analyzed 14 articles, and focused on older adults over
65 years old [10]. The most recent review on a topic most likethis work was published 5 years ago, spanned 10 years, analyzed
45 articles, and focused on older adults aged over 50 years [11].
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Kruse et al
With an aging population, telehealth services are becoming
more common to aid in independent living and healthmanagement [14]. An example of telehealth is virtual homehealth care, where health care providers provide guidance in
specific procedures while the patients are in the comfort of theirhome. Telehealth programs can improve access to health careand have a positive effect on patients’ medical outcomes,
especially for the treatment of chronic illnesses in vulnerable
populations, such as elderly people [15]. Utilizing age-friendly
technology could improve the care providers give to older adults
through telehealth services and improve the usability oftelehealth for older adults [16]. It is essential to first understandthe barriers that affect the usability of telehealth services amongolder adults in order to find opportunities for improving healthoutcomes. Barriers to using telehealth can affect the accessibilityof health services to older adults. When it comes to technology,
older adults are often stereotyped as laggards in technology
adoption [7]. However, owing to rising telehealth capabilities,
improvement of access, especially to older adults, can aid in
improving health outcomes [15]. Understanding the perspectivesof older adults is important when evaluating telehealth barriers
because older adults generally develop different perspectives
compared with those of other age demographics [16]. Other
studies on this topic have focused on conditions like depression,
heart failure, and falls [17-19]. However, no review has looked
at medical outcomes, including indicators of quality of life, thatcome as a benefit of using telehealth and the barriers that exist
to the use of telehealth internationally. This review intends toexamine these issues and what has changed in telehealth for
older adults in the last 5 years.

Objectives
The purpose of this systematic review was to evaluate thecurrent literature to help identify and understand health-related
quality of life enhancers and general health outcomes that arecommensurate with and barriers to the use of telehealth services
by older adults. Health outcomes, including quality of life
enhancers, provide the “so what” to the use of telehealthmodalities. Recognizing barriers can help develop solutions forbroadening the use of telehealth services in older adults. During
the COVID-19 crisis, providers and patients alike were thrust
into the world of telehealth. An overview of the benefits andbarriers would be helpful to those deciding whether to continue
the use of telehealth modalities.

Methods
Protocol and Registration
This review used the Kruse protocol published in 2019 and thePreferred Reporting Items for Systematic Reviews andMeta-Analyses (PRISMA) guidelines [20,21]. The review wasregistered with PROSPERO on May 2, 2020 (ID:CRD42020182162). In accordance with the rules at
PROSPERO, the registration was completed before analysis
began.

Eligibility Criteria
Studies were eligible for this review if participants were older
adults (older than 50 years), if the intervention was some form
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Kruse et alof telehealth (including mHealth, eHealth, and all forms oftelehealth), if the authors reported either barriers to the use of
telehealth or health outcomes, and if the article was publishedin a research journal in the English language in the last 5 years.
Adults older than 50 years were chosen out of trial and error.
When we initially wrote the methods for this study, we chosea more universal definition of older adults as those over 65 yearsof age. Once we started filtering articles for analysis, we noticed
a large number of articles that were being eliminated, despite
the high level of quality of these studies. If we had stuck withage over 65 years as our screening criteria, we would haveeliminated more than half of the group of articles for analysis.
As a result, we chose age over 50 years, which is supported byother reviews in this field [11]. This is a limitation we list later.

several standard items used for summary, such as PICOS(Participants, Intervention, Comparison [to the control group],
Outcome, Study design), and analysis, such as forms oftelehealth interventions, barriers to the use of telehealth by older
adults, and the medical outcomes observed in older adults using
telehealth solutions [20]. After making a list of observations,reviewers attempted to make sense of the observations using anarrative analysis [22].

Information Sources

Data Items

The following four databases were queried: Cumulative Index
of Nursing and Allied Health Literature (CINAHL), PubMed(MEDLINE), Web of Science (WoS), and Embase (Science
Direct). Additionally, a specific journal search was conductedin the journal of choice for publication (Journal of Medical
Internet Research). Databases were filtered for the last 5 years.
Database searches occurred between February 2 and 14, 2020.
A period of 5 years was chosen because it has been that longsince the last review was published on a similar topic. We expect
to find advances in technology and advances in adoption byelderly people because younger people who use technology
regularly have advanced into the observation group of over 50
years old. We hope to find fewer barriers.

The piloted form collected data, including participants,
intervention, study design, results compared to a control group(where applicable), medical outcomes, sample size, bias withinstudies, effect size, country of origin, statistics used, barriers to
the use of telehealth, and quality assessment from the John
Hopkins Nursing Evidence-Based Practice (JHNEBP) rating
scale, as well as general observations about the article that would
help in interpretation of the results [23]. These data items wereindependently collected and discussed in the second consensus
meeting.

SearchReviewers carefully analyzed the MEDLINE Medical Subject
Headings (MeSH) for key terms related to telehealth and elderly
people. Based on the established hierarchy of indexed terms at
MeSH and a series of experimental searches, the final searchterms were “Telehealth AND ‘older adults.’” This combinationof terms yielded the maximum number of results in all four
databases. Reviewers used available filters to eliminate otherreviews and focus on academic or peer-reviewed journals overthe last 5 years.

Study Selection
Reviewers followed the Kruse protocol, which entails a seriesof three consensus meetings. The results of the first consensus
meeting identified the studies for analysis. After filtering theresults of the four databases to meet the eligibility criteria, allreviewers screened the abstracts of the results to ensure that
articles were germane to the topic, they were actually studies(not protocols), and they contained tangible results to enable
analysis toward the review’s objectives. The first consensus
meeting discussed whether to keep articles for analysis. The
reasons for rejection included opinion article (not a study),
protocol (no results), concept or design paper (no results),
review, no use of telehealth, and no reporting of either outcomesor barriers. A kappa statistic was calculated from the results ofthis meeting [20]. Before consensus meeting number two, thegroup leader assigned workload to ensure that each article wasanalyzed by at least two reviewers. Reviewers independently
analyzed articles using a piloted form. Reviewers collected
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Data Collection Process
The group leader divided analysis workload to ensure all articles
were reviewed by at least two reviewers. Reviewers
independently analyzed articles using a standardized Excel
spreadsheet as a piloted form for data extraction.

Risk of Bias Within and Across Studies
General observations of bias were made about each study, suchas selection bias. These observations were independently
collected and discussed in the second consensus meeting. The
JHNEBP rating scale was used to assess the risk and quality ofeach study analyzed. Within the JHNEBP rating scale, level I
indicates experimental studies, randomized controlled trials(RCTs), or meta analyses of RCTs; level II indicatesquasiexperimental studies; level III indicates nonexperimental
studies, qualitative studies, or meta-syntheses; level IV indicates
opinions of nationally recognized experts based on research
evidence or expert consensus panels (systematic reviews orclinical practice guidelines); and level V indicates opinions ofindividual experts based on nonresearch evidence. There arethree levels of quality of evidence, which are listed as A (highquality), B (good quality), and C (low quality or major flaws).
Each of these levels define the following four thresholds:
research, summative reviews, organizational opinion, and expert
opinion. For instance, in level A, studies have consistent resultswith sufficient sample size, adequate control, and definitive
conclusions. In level C, studies have little evidence withinconsistent results and insufficient sample size, and conclusions
cannot be drawn. To limit the inherent bias and limitationscommensurate with low-quality studies, the ratings from theJHNEBP rating scale serve as screening criteria. Articles withevidence ratings below level IV were not accepted. Quality ofevidence ratings below level B were highly suspect.

Summary Measures and Additional Analysis
The review analyzed both qualitative and quantitative methods,
so the summary measures sought were not consistent. The
preferred summary statistic was the risk ratio, but other summary
statistics were also sufficient. The summary statistics were
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independently collected and discussed in the second consensus
meeting.

Results

A narrative analysis summarized themes for barriers,
interventions, and medical outcomes. They were reported insummary statistics in affinity matrices. These themes wereindependently collected and discussed in the third consensus
meeting. After themes were identified, interactions between
themes were observed using a spreadsheet.

Study Selection
Figure 1 illustrates the study selection process. A kappa statistic
was calculated to measure the reliability of article selection
between reviewers. The κ value was 0.75, representing moderateagreement [24,25].

Figure 1. Study selection process.

Study Characteristics

2019 [5,26-34], 2018 [4,15,16,35-46], 2017 [14,47-56], 2016
[19,57-63], and 2015 [64-76].

Table 1 lists the ancillary data extracted from the studies
analyzed in reverse chronological order as follows: 2020 [26],http://medinform.jmir.org/2020/8/e20359/

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Table 1. PICOS characteristics.
Authors, year

Participants

Intervention

Comparator

Medical outcomes reported Study design

Hamilton et al, 2020[26]

765 older adults; ≥55
years; Medicare/Medicaid
beneficiaries; English 76%(581), Spanish 20% (153),
and others 4% (31); low
incomeTelemonitoring

None

Hospital visits and readmis- Observational
sions
study

None

Satisfaction: 64% (353) of Analytical obserolder adults were satisfied vational study
with the health information
they received, 34% (187)were neutral, and 2% (11)were dissatisfied

Remote patient monitoring(RPM): blood-pressure cuffs,
pulse oximeters, and bodyweight scales
Telehealth Intervention Programs for Seniors, RPM, extensive social wraparound services, care coordination, andintergenerational socialization
aimed at improving health care
options to assist low-income
high health risk older adults
who live in subsidized congregate housing or attend local
community centers for older
adults.
A survey instrument was collected each week.

Theis et al, 2019 [5]551 older adults, ≥60
years, 51.3% male and48.7% female

eHealth441 participants (80%) already retired, 109 (19.8%)
still working
Wildenbos et al, 2019[27]

13 older adults, ≥50 years, mHealthaNoneprimarily Dutch speaking
Investigated these interaction
Additional inclusion crite- issues in two different case
ria for App 2: heart failure studies; an app for older adults(HF) patient and chronic
facilitating their hospital apobstructive pulmonary dis- pointment attendance (App 1)
ease
and a self-monitoring app forchronically ill older patients

Cognitive impairment was Case study
reported but not comparedwith a control.

Jakobsson et al, 2019[28]

9 older adults, 65-85 years, Telehealth, smartphone, comcognitive impairment ofputer, and landline
different origin (eg, stroke,dementia, and mild cognitive impairment)

None

Cognitive impairment was Qualitative study
reported but not comparedwith a control.

None

Safety, satisfaction, securi- Qualitative study
ty, independence, responsibility, mindfulness of failty

Karlsen et al, 2019 [29] 18 older adults, ≥60 years,
living in their own homes
and having recently received telecare service(within the last 0-3
months), received homecare services, Norwegianspeaking, no limitations
considering disease or
chronic conditions

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Telemonitoring
Personal alarm (16), light sensors (3), stove alarm (4), GPStracking (3), medication reminders (8), bed sensors (1),door sensor (2), video surveillance (2)JMIR Med Inform 2020 | vol. 8 | iss. 8 | e20359 | p. 5
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Authors, year

Participants

Intervention

Comparator

Medical outcomes reported Study design

Coley et al, 2019 [30]341 (quantitative) and 46(qualitative) older adults;≥65 years; Finland, France,and Netherlands; response
rate 79% (Finland: 81%,
France: 72%, Netherlands:
87%, P=.04); 48% (164)male; 51% (174) university-level education

eHealthControl

Not reported

Cross-sectional
mixed-methods
randomized controlled trial(RCT)

Giesbrecht & Miller,
2019 [31]

18 older adults, ≥50 years,
resided in the community,
self-propelled using both
hands at least 1 hour per
day inside and outside
their home, English speaking

eHealthControl

Skill capacity and safety

RCT

Brodbeck et al, 2019[32]

110 older adults, >50
years, 79% (87) female

eHealthControl

Grief, depression, psycho- Mixed methods,
logical distress, embitter- RCT
ment, loneliness, and life
satisfaction

Mosley et al, 2019 [33] 112 older adults, ≥60
years, 58% (65) female,
English speaking

eHealthControl

Not reported

Quasiexperimental study

Jensen et al, 2019 [34]eHealthNone

Autonomy and self-care

Qualitative study

Not reported

Quasiexperimental study

20 older adults, hip fracture

Participants were randomized
to either an interactive internet
platform designed to encourage
goal setting and lifestylechanges with the remote support of a lifestyle coach or acontrol platform with basic
health information but no interactive features or coach support. Owing to the nature of theintervention, complete double
blinding was not possible, but
masking was attempted by informing participants that theywould be randomized to one of
two internet platforms (without
further details on the content).

The treatment group incorporated two in-person training sessions with a trainer and 4 weeks
of monitored home training using a computer tablet (mHealth)wheelchair skills program. The
control group did not receive
skills training, as is typicalpractice with this population.

Internet-based self-help intervention for prolonged grief
symptoms after spousal bereavement or separation/divorce

Etymotic home hearing testcompared with traditional
manual audiometry“My Hip Fracture Journey” on
iPad (provided) education
through pictographs, video
clips, illustrated exercises, andwritten information. This wasused to augment home visitsand subsequent interviews.

Rasche et al, 2018 [15] 576 older adults, ≥60
eHealthControl
years, 48.7% (280) female, The national survey queried theGerman speaking
use of health apps and theirperceived usefulness.
Portz et al, 2018 [35]30 older adults, ≥60 years,
location at the Universityof Colorado Hospital andthe University Hospital
Cleveland Medical Centerin Cleveland (Ohio), 60%(18) female, 63% (19)black people

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mHealthThe HF app was developed toallow patients to track their
symptoms of HF. Thirty older
adults completed an acceptability survey after using the mobile app. The survey used Likert items and open-ended feedback questions.

None

Awareness of the condition Quantitative acand self-care
ceptability survey
analysis

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Authors, year

Participants

Intervention

Comparator

Castro et al, 2018 [36]501 older adults, ≥65
eHealthPretestyears, Medicare population Participants were matched into
geographically based small
groups with an assigned healthcoach, and they began the program at the same time. Group
members were connected toeach other through a private
online social forum where they
could post comments andquestions, engage in healthcoach–moderated discussions,and provide social support toone another.

Medical outcomes reported Study design
Weight: participants lostan average of 13-14
pounds (8%)

Single-arm
pretest/posttest
designHbA1c: 0.14% absolutedecrease at 6 months and
12 months (P<.001)
Cholesterol: mean reduction of -12.92 mg/dL(P<.001).

Using internet-enabled devices(laptop, tablet, or smartphone),
program participants were able
to asynchronously complete
weekly interactive curriculum
lessons, reflections, and goalsetting activities in relation to
the weekly topic.
Joe et al, 2018 [37]43 older adults, 70% (30)femaleeHealthNone

Not reported

Qualitative analysis study (focus
groups)A focus-group method wasused to brainstorm designs fortelehealth for older adults.

Dham et al, 2018 [4]134 older adults, 60% (80) Telemedicine
femaleTelepsychiatry assessments

None

Not reported

Mixed-methods
cross-sectional
cohort study withretrospective
chart review andprospective feedback survey

Paige et al, 2018 [16]384 older adults, 74.3%
eHealthNone(285) female, 57.7% (222) eHealth awareness and eHealth
Caucasian people, 42.3% literacy scale(162) black people

Not reported

Qualitative measurement invariance study

Cajita et al, 2018 [38]

10 older adults, ≥65 years, mHealthhistory of HF, spoke English, difficulty with mobile technology

None

Not reported

Descriptive exploratory study

Harte et al, 2018 [39]

22 older adults, >65 years, mHealthNonedifficulty using smartTraining on a smartphonephones
based fall detection and prevention system

Not reported

Usability andlearnability casestudy

Gordon & Hornbrook,2018 [40]2602 older adults, >65
eHealthyears, 54% (1,405) female, Online forms, online tracking
79% (2,056) Caucasiansystems, and patient portal
peopleNone

Not reported

Mixed-methods
cross-sectional
study

Bao et al, 2018 [41]

12 older adults, ≥65 years, eHealth75% (8) female
Online training

PretestSensory organization test, Pretest posttest
mini balance evaluation
true experiment
system test, five times sit
to stand test, and no statistical significance in otherclinical outcomes

Egede et al, 2018 [42]241 older adults, >63
Telemedicine
years, 98% (236) male,
Telepsychotherapy
60% (144) Caucasian people, veterans having major
depressive disorder

Control

Baseline depression sever- RCT
ity, generalized anxiety
disorder, alcohol misuse,cannabis misuse, andcannabis dependence

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Authors, year

Participants

Platts-Mills et al, 2018[43]

Kruse et al
Intervention

Comparator

Medical outcomes reported Study design

75 older adults, <50 years, Telecare
musculoskeletal pain
Telephone call and protocolguided follow up

Control

Pain

Lopez-Villegas et al,2018 [44]50 older adults, >65 years, Telemonitoring
48% (24) women, seen in Pacemakers
the cardiology clinic, usinga pacemakerControl

EQ-5D VASb (health-relat- RCT
ed quality of life)

Dugas et al, 2018 [45]

27 older adults, >60 years

None

Glucose management and
HbA1c

None

HbA1c, independence,
Qualitative pilot
programemotional support, andmotivation to self-manage

Buck et al, 2017 [47]

12 older adults, >60 years, eHealthNone
42% (5) female
PSHA, a web-based tablet-delivered intervention developed
internally, which encourages
the participant to record daily
medication intake, weight, andtime spent with a brief exercise
program using an aerobic stepper. The tablet records daily
information, and the participant
watches a short heart healtheducational video.

Documentation for nutri- Proof-of-concept
tion and eating and instruc- trial
tional video exposure
Qualitative
semistructured
interviews after
the study protocol

Ware et al, 2017 [14]15 older adults, ≥50 years, eHealth73% (11) female

None

Not reported

Chang et al, 2017 [48]

18 older adults, >65 years, Telehealthdiabetes
Diabetes management

None

Self-management and inde- Qualitative rependence
search design and1-1 semistructured interviews

Cajita et al, 2017 [49]129 older adults, >65
years, 73.6% (95) male,
56.6% (73) CaucasianpeopleNone

Not reported

Cross-sectionalcorrelational
study

MemoryQualitative study

mHealthDiaSocial for glucose control,
exercise, nutrition, and medication adherence

Nalder et al, 2018 [46]

8 older adults, >55 years,
type 2 diabetes

eHealthThree internet-based platforms:
1. Chronic disease management

Randomized controlled pilot study

Pilot study

2. Real-world strategy training
3. Learning the ropes

LaMonica et al, 2017[50]

mHealth
Simple linear regression wasused to test the relationship between the main study variables(eHealth literacy, social influence, perceived financial cost,
perceived ease of use, and perceived usefulness) and intention
to use mHealth.

221 older adults, ≥50
eHealthNone
years, 57.7% (128) female Memory aids and mental acuity
exercises

Bahar-Fuchs et al, 2017 45 older adults; >65 years;[51]
mild cognitive impairment(n=9), mood-related neuropsychiatric symptoms(n=11), or both (n=25)

eHealthNahm et al, 2017 [52]eHealth866 older adults, >50
years, bone health issues

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Control

Memory, global cognition, RCTlearning, and moodControl

Osteoporosis knowledge,
self-efficacy/outcome expectations, and exercise
behaviors

Tailored and adaptive computer
cognitive training in older
adults at risk for dementia

Bone Power program

Two focus
groups and pragmatic thematic
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Authors, year

Participants

Kruse et al
Intervention

Comparator

Medical outcomes reported Study design

Knaevelsrud et al, 2017 47 older adults, >50 years, eHealthControl[53]
64.9% (31) female, post- Internet-based therapist-guided
traumatic stress disorderintervention
symptoms, German speaking

Comfort (from not being
able to see the therapist),
satisfaction, motivation,
feeling of being understoodReijnders et al, 2017[54]376 older adults, >50
years, 67.5% female

eHealthControlFeelings of stability, mem- RCT
ory functioning, and locus
of control

Hamblin et al, 2017[56]

60 older adults, >85 years

Telemonitoring

None

Autonomy, awareness ofdanger areas like gardens
or staircases, and safety

Mixed-methods
study

Mageroski et al, 2016[55]

25 older adults, >50 years

Telemonitoring

None

Not reported

Mixed-methods
study

Wang et al, 2016 [57]

29 older adults, >65 years, TelemonitoringNone
71% (21) female
Wearables, mobile devices,trackers, and in-home telemonitoring

Not reported

Cross-sectional
study

Gordon & Hornbrook,2016 [58]231,082 older adults fordatabase arm, 2602 older
adults for survey arm

eHealthNone

Not reported

Mixed methods,
database, and survey study

Williams et al, 2016[59]

7 older adults, >60 years,
dementia

eHealthNone

Not reported

Pilot study

Evans et al, 2016 [19]41 older adults, >55 years, mHealth57.1% (23) female, EnRemote monitoring, wrist
glish speaking
wearable, and wireless tablet

ControlDocumentation for weight Single-arm quasiand blood pressure
experimental
study

Muller et al, 2016 [60]43 older adults, ≥55 years, mHealthControlmobile phone use, no regu- SMS and Physical Activity forlar exercise
Health Study

Exercise, mood, fitness,health, mindfulness of theimportance of exercise,and guilt

Two-arm parallel
RCT

Quinn et al, 2016 [61]118 older adults, >50
years, 66% (78) female,diabetes

mHealthControl

HbA1c

True experiment

Royackers et al, 2016[62]

8 older adults, caring for
loved ones in their lastdays

eHealthNone

Comfort, independence,
and autonomy

Qualitative pilot
study

Duh et al, 2016 [63]45 older adults, >60 years

TelecareNone

Not reported

Qualitative participatory design

Depatie & Bigbee, 2015 30 older adults, ≥60 years, mHealth
None
[64]
80% (24) female
Mobile health technology for
older adults in rural communities

Not reported

Mixed-methods
study

Moore et al, 2015 [65]26 older adults, >55 years, eHealth77% (20) maleInternet-based hearing healthcare for older adults

None

Not reported

Training study

Currie et al, 2015 [66]168 older adults, ≥60
eHealthyears, living in rural areas,
long-term chronic pain

None

Pain

Mixed-methods
study

Cognitive functioning

Remote sensors in homes ofolder adults

Mobile diabetes intervention
study

Point of care technology
through eShift (home-based
palliative care)

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Authors, year

Participants

Intervention

Comparator

Medical outcomes reported Study design

Grant et al, 2015 [67]762 older adults, >60
years, 67% (511) female,
90% (686) Caucasian people

Telemonitoring

Control

Satisfaction, autonomy,
and independence

RCT

141 older adults, ≥60
years, 81% (114) female,living in rural areas, diagnosis of generalized anxiety disorder (GAD)

TelecareControl

Worry, GAD, depression,and anxiety

RCT

ControlReasoning, verbal learnRCT
ing, and instrumental activities of daily living

Control

Depressive symptoms,
anxiety symptoms, andMH functioning

Brenes et al, 2015 [68]Corbett et al, 2015 [69] 2192 older adults, ≥60
years

LivingWell@Home, sensors(motion, bed, and humidity),emergency response systems,
and biometric monitors (heartrate, blood pressure, weight,
pulse oximetry, and blood glucose)

Telephone-delivered cognitive
behavior therapy and telephone-delivered nondirective
supportive therapy
eHealthOnline cognitive training package

Mavandadi et al, 2015
[70]1018 older adults, ≥65
years, 83.2% (847) female,community-dwelling, lowincome, mental healthsymptoms

Egede et al, 2015 [71]90 older adults, ≥58 years, Telemedicine
98% (88) male, diagnosis Telepsychotherapyof diabetes

Control

Geriatric depression scale, RCT
Beck depression inventory,and Diagnostic and Statistical Manual, version 4
symptoms

Chang et al, 2015 [72]192 older adults, >60
years, 81% (156) female,cardiology diagnoses

Telemonitoring

None

Cardiac arrhythmias detect- Pilot study
ed and paroxysmal atrial
fibrillation detected

27 older adults, 31 caregivers, 43 healthcare professionals

eHealthNone

Communication of the
Pilot study
condition with the provider

None

Not reported

Structured equation modeling
Randomized feasibility study

Boulos et al, 2015 [73]aKruse et al

TelecareSUSTAIN care management
system (assessment, monitoring, care management, andbrief therapies)

Remote cardiology management

LiveWell Parkinson intervention and learning modules

RCT

Dino & deGuzman,2015 [74]82 older adults, demograph- Telemedicine, mHealth, andics not reported
eHealth

Czaja et al, 2015 [75]

24 older adults, >60 years, Telemonitoring
Control
71% (17) female, 94% (23) Telehealth system that monitors
Hispanic people, diagnosis blood pressure and body weightof hypertension

Self-management, health,and independence

Choi et al, 2015 [76]42 older adults, ≥60 years,
81% (34) female, low-income, homebound, scoreof 15 or above on the 24item Hamilton Rating
Scale for Depression

Depressive symptoms, un- Qualitative
derstanding of depression,and social interaction

Telecare

None

Video tele-problem-solving
therapy (PST) to in-person PST
and telephone care calls

mHealth: mobile health.

b

EQ-5D VAS: European health-related quality of life utility with a visual analogue scale.

Risk of Bias Within Studies
At the study level, reviewers recorded observations of bias. The
most frequently observed form of bias was selection bias (asking
for volunteers for a research study involving technology will
result in volunteers who already gravitate toward technology),
which occurred in 7 out of 57 (13%) articles analyzed[15,26,30-32,37,39]. There were six instances of conveniencesamples from a local population [34,49-52,64]. Both examplesof bias limit the external validity of the results.
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Results of Individual Studies
Themes that resulted from the narrative analysis are listed inTable 2. Repetition can be observed in a frame of a theme owing
to multiple observations from the same article for that theme.
Translations from observations to themes for interventions,medical outcomes, and barriers are listed in Multimedia
Appendix 1, Multimedia Appendix 2, and Multimedia Appendix
3, respectively. These appendices illustrate the logical inference
reviewers made for each theme. For instance, one article listed
remote patient monitoring for blood pressure, pulse oximeter,
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JMIR MEDICAL INFORMATICS
and body weight scales. These were categorized undertelemonitoring [26]. The same article listed a decrease in
hospital visits and a decrease in readmissions. These werecategorized under an increase in hospital metrics. Additional

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Kruse et aldata collected (bias, statistics, country of origin, and quality
assessments) are displayed in Multimedia Appendix 4. In
consensus meeting number two, we identified generalobservations, as depicted in the tables [20].

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Kruse et al

Table 2. Summary of the analysis of each article.
Authors, year

Intervention

Medical outcome theme

Theme of barriers

Hamilton et al, 2020 [26]Telemonitoring

Increase in hospital metrics

Not reported

Theis et al, 2019 [5]eHealthIncrease in satisfaction

Medical literacy
Trust of the internet
Ownership of technologya

Wildenbos et al, 2019 [27]mHealthb

Visual acuitya

Increase in cognitive ability

Mental acuity
Technical literacy
Jakobsson et al, 2019 [28]mHealthIncrease in cognitive ability

Social implications

eHealthPrivacy and securitya

Telecare (phone)Technical literacy
Lack of desire
Ownership of technology
Lack of technical support

Karlsen et al, 2019 [29]Telemonitoring

Increase in safety or security

Mental acuity

Increase in health-related quality of life
Increase in safety or security

Visual acuityaSocial implications

Increase in autonomy
Increase in mindfulness of the condition
Coley et al, 2019 [30]eHealthNot reported

Trust of the internet

Giesbrecht & Miller, 2019[31]

eHealthIncrease in cognitive ability

Not reported

Brodbeck et al, 2019 [32]eHealthIncrease in safety or security
Decrease in psychological distressa

Not reported

Increase in health-related quality of life
Mosley et al, 2019 [33]Jensen et al, 2019 [34]eHealtheHealthNot reported

Costa

Privacy and security

Increase in autonomy

Ownership of technology
Lack of desire
Lack of technical support
Technical literacy
Rasche et al, 2018 [15]eHealthNot reported

Trust of the internet
Technical literacya
Privacy and security

Portz et al, 2018 [35]mHealthCastro Sweet et al, 2018 [36] eHealth

Increase in mindfulness of the condition

Technical literacy

Increase in autonomy

Medical literacy

Decrease in medical conditions surrounding dia-

Not reported

betesaJoe et al, 2018 [37]eHealthNot reported

Visual acuitya
Hand-eye coordination
Technical literacy
Lack of desire

Dham et al, 2018 [4]Telehealth video call

Increase in satisfaction

Visual acuity
Auditory acuity

Paige et al, 2018 [16]eHealthNot reported

Technical literacy
Trust of the internet

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Authors, year

Intervention

Medical outcome theme

Theme of barriers

Cajita et al, 2018 [38]mHealthNot reported

Medical literacy
Mental acuity
Lack of desire
Technical literacy
Ownership of technology
Cost

Harte et al, 2018 [39]mHealthNot reported

Technical literacy

Gordon & Hornbrook, 2018[40]

eHealthNot reported

Cost

Bao et al, 2018 [41]eHealthTechnical literacy
Increase in cognitive ability

Not reported

Increase in activity or coordination
Egede et al, 2018 [42]Telehealth video callaDecrease in psychological distressa

Not reported

Decrease in medical conditions surrounding paina
Platts-Mills et al, 2018 [43]Telecare (phone)

Decrease in medical conditions surrounding pain

Not reported

Lopez-Villegas et al, 2018[44]

Telemonitoring

Increase in health-related quality of life

Not reported

Dugas et al, 2018 [45]mHealthDecrease in medical conditions surrounding dia-

Not reported

betes
Nalder et al, 2018 [46]eHealthaDecrease in medical conditions surrounding diabetes

Technical literacy

Increase in autonomy
Decrease in psychological distress
Increase in autonomy
Buck et al, 2017 [47]eHealthIncrease in documentation to give the provider

Technical literacy

Increase in mindfulness of the condition
Ware et al, 2017 [14]eHealthNot reported

Trust of the internet
Medical literacy
Technical literacy
Social implications
Lack of technical support
Privacy and security

Chang et al, 2017 [48]mHealthIncrease in autonomya

Cost

Cajita et al, 2017 [49]mHealthNot reported

Medical literacy
Lack of desire
Cost
Technical literacy
Social implications

LaMonica et al, 2017 [50]eHealthIncrease in cognitive ability

Auditory acuity
Cost
Auditory acuity

Bahar-Fuchs et al, 2017 [51]eHealthaIncrease in cognitive ability

Not reported

Decrease in psychological distress
Nahm et al, 2017 [52]eHealthIncrease in mindfulness of the condition

Not reported

Increase in autonomy
Increase in activity or coordination

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Authors, year

Intervention

Medical outcome theme

Theme of barriers

Knaevelsrud et al, 2017 [53]eHealthIncrease in safety or security

Not reported

Increase in satisfaction
Increase in autonomy
Increase in health-related quality of life
Reijnders et al, 2017 [54]eHealthIncrease in activity or coordination

Not reported

Increase in cognitive ability

Privacy and security

Increase in autonomy
Hamblin et al, 2017 [56]Telemonitoring

Increase in autonomy

Technical literacy

Increase in mindfulness of the condition

Medical literacy

Increase in safety or security

Social implicationsa

Mageroski et al, 2016 [55]Telemonitoring

Not reported

Cost

Wang et al, 2016 [57]Telemonitoring

Not reported

Lack of desire

Gordon & Hornbrook, 2016
[58]

eHealthNot reported

Ownership of technology
Lack of technical support
Cost
Technical literacy
Hand-eye coordination
Trust of the internet
Social implications
Lack of desire

Williams et al, 2016 [59]eHealthNot reported

Technical literacy
Lack of technical support
Mental acuity
Visual acuity
Hand-eye coordination

Evans et al, 2016 [19]mHealthIncrease in documentation to give the provider

Lack of desire
Technical literacy
Lack of desire
Ownership of technology

Muller et al, 2016 [60]mHealthIncrease in activity or coordinationa

Lack of desire

Decrease in psychological distress
Decrease in medical conditions surrounding diabetes
Increase in mindfulness of the condition
Increase in guilt
Quinn et al, 2016 [61]Royackers et al, 2016 [62]mHealtheHealthDecrease in medical conditions surrounding diabetes

Visual acuity

Increase in safety or security

Not reported

Auditory acuityaIncrease in autonomy
Duh et al, 2016 [63]Telecare (phone)

Not reported

Mental acuity
Lack of desire
Lack of technical support
Technical literacy
Cost

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Authors, year

Intervention

Depatie & Bigbee, 2015 [64] mHealth

Kruse et al
Medical outcome theme

Theme of barriers

Not reported

Cost
Lack of desire
Social implications
Technical literacy
Lack of technical support
Privacy and security

Moore et al, 2015 [65]eHealthNot reported

Technical literacy
Computer anxiety
Lack of technical support

Currie et al, 2015 [66]eHealthDecrease in medical conditions surrounding pain

Social implications

Grant et al, 2015 [67]Telemonitoring

Increase in health-related quality of life

Lack of desire

a

Cost

Increase in autonomy

Privacy and security
Brenes et al, 2015 [68]Telecare (phone)

Decrease in psychological distressa

Not reported

Corbett et al, 2015 [69]eHealthIncrease in cognitive abilitya

Not reported

Increase in health-related quality of life
Mavandadi et al, 2015 [70]Telecare (phone)

Decrease in psychological distressa

Not reported

Egede et al, 2015 [71]Telehealth video call

Decrease in psychological distressa

Not reported

Chang et al, 2015 [72]Telemonitoring

Increase in mindfulness of the conditiona

Not reported

Boulos et al, 2015 [73]eHealthIncrease in documentation to give the provider

Medical literacy
Lack of technical support
Mental acuity
Hand-eye coordination
Visual acuity

Dino & deGuzman, 2015 [74] mHealth

Not reported

Lack of desire

eHealthLack of technical support

Telemonitoring
Czaja et al, 2015 [75]Telemonitoring

Increase in autonomya

Technical literacy

Decrease in medical conditions surrounding diabetes
Choi et al, 2015 [76]Telehealth video call

Decrease in psychological distress

Ownership of technology

Increase in mindfulness of the condition

Lack of desire

Increase in autonomyaMultiple uses of this theme in the same article. See Multimedia Appendix 1 for a complete list of individual observations and their translation to themes.

bmHealth: mobile health.

Risk of Bias Across Studies and Quality Assessments
Table 3 summarizes the quality indicators identified by the
JHNEBP tool [15]. The most frequent strength rating was III,
followed by I, II, and IV. The most frequent evidence rating
was A, followed by B and C. No strengths below IV were

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encountered. A full list of quality assessments is presented in
Multimedia Appendix 4. Articles that did not meet the minimum
standards of quality were not included in the analysis. Thisdecision was made to limit the bias inherent to nondata-driven
opinions or conclusions that do not logically follow the data.

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Table 3. Summary of quality indicators.
Quality indicator

Value (N=57), n (%)

Strength of evidence
I (experimental study, RCTa, or meta-analysis of RCT)

18 (32%)

II (quasiexperimental study)

10 (17%)

III (nonexperimental, qualitative, or meta-synthesis study)

28 (49%)IV (opinion)

1 (2%)

Quality of evidenceaA (high quality)

33 (58%)B (good quality)

23 (40%)C (low quality or major flaws)

1 (2%)

RCT: randomized controlled trial.

Additional Analysis
The results of consensus meeting three identified the themesthat corresponded with telehealth interventions, barriers to theuse of telehealth, and medical outcomes. These are summarizedin Tables 4-6.

Interventions of TelehealthFive themes for interventions were identified. Two studies used
multiple telehealth interventions. Table 4 lists the interventions
with the associated references, number of occurrences, andprobability of occurrence in the review. The most common
intervention was eHealth (computer driven), followed bymHealth (smart device driven), telemonitoring (remote sensors),
telecare (phone), and telehealth video call.

Table 4. Affinity matrix for telehealth interventions.

aIntervention

References

Number of occurrences Probability of occurrence(N=62)

eHealth[5,14-16,28,30-34,36,37,40,41,46,47,50-54,58,59,62,65,66,69,73,74]

29

47%

mHealtha[19,27,28,35,38,39,45,48,49,60,61,64,74]

13

21%

Telemonitoring[26,29,44,55-57,67,72,74,75]

10

16%

Telecare (phone)[28,43,63,68,70]

5

8%Video call

[4,29,42,71,76]

58%

mHealth: mobile health.

Medical Outcomes and Health-Related Quality of Life
Enhancers
Thirteen themes and one observation that did not correspondwith a theme for medical outcomes and quality of life factors
were reported. Table 5 lists the outcomes with their associatedreferences, number of occurrences, and probability of occurrencein this review. The most common theme for medical outcomes
associated with telehealth interventions was decrease inpsychological distress (decrease in anxiety symptoms, decreasein depressive symptoms, decrease in embitterment, decrease ingrief, decrease in worry, decrease in loneliness, increase inemotional support, and increase in mood), with 21 of 118 (18%)
occurrences [32,42,46,51,60,68,70,71,76]. The theme associatedwith quality of life factors was listed as an increase in autonomy(increase in locus of control, increase in autonomy, increase in
responsibility, increase in motivation to self-manage, andincrease in independence), with 18 of 118 (15%) occurrences[29,34,35,46,48,52-54,56,62,67,75,76]. One theme wasassociated with an increase in cognitive ability (increase in skill
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ability, increase in sensory organization, increase in memory,increase in cognitive activity, and increase in reasoning), with
11 of 118 (9%) occurrences [19,20,23,32,41,42,60]. Anothertheme was associated with a decrease in symptoms surrounding
diabetes (decrease in HbA1c, decrease in cholesterol, increasein glucose management, and increase in diabetes health), with
9 of 118 (8%) occurrences [28,36,37,51,52,66]. Another theme
was associated with an increase in mindfulness of the condition(increase in medical events detected, increase in education
exposure, and more awareness of danger areas for falls likeoutside or stairwells), with 8 of 118 (7%) occurrences[21,27,38,43,47,51,63,67]. The next theme summarized
observations of an increase in the sense of safety, security, or
comfort, with 7 of 118 (6%) occurrences [21,23,44,47,53]. The
last set of themes comprised 25% of the observations, and they
were an increase in health-related quality of life (increase inlife satisfaction and increase in the feeling of being understood);
increase in activity or coordination (increase in mobility,
increase in activity, increase in exercise, decrease in weight,
decrease in BMI, increase in balance evaluation, and increase
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Kruse et alin the feeling of stability); decrease in medical conditions
surrounding pain (decrease in alcohol abuse, decrease incannabis misuse, decrease in cannabis dependence, and decreasein pain); increase in documentation to give to the provider(documentation and communication with the provider); increasein satisfaction (satisfaction with the health care system); andincrease in hospital metrics (decrease in readmissions anddecrease in hospital visits). The last observation was the only
negative outcome. One participant noted that the SMS text
messages she received as part of an exercise RCT increased
her level of guilt because she was not exercising.

Table 5. Affinity matrix for medical outcomes and quality of life factors observed by older adults using telehealth.
Theme or observation

References

Number of occurrences(N=118)Probability of occurrence

Decrease in psychological distress[32,42,46,51,60,68,70,71,76]

21

19%

Increase in autonomy[29,34,35,46,48,52-54,56,62,67,75,76]

18

16%

Not reported[14-16,30,33,37-40,49,55,57-59,63-65,74]

18

16%

Increase in cognitive ability[27,28,31,41,50,51,54,69]

11

10%

Decrease in medical conditions surrounding [36,45,46,60,61,75]
diabetes

98%

Increase in mindfulness of the condition[29,35,47,52,56,60,72,77]

8

7%

Increase in safety or security[29,31,53,56,62]

7

6%

Increase in health-related quality of life

[29,32,44,53,67,69]

6

5%

Increase in activity or coordination[41,52,54,60]

6

5%Decrease in medical conditions surrounding [42,43,66]
pain

5

4%

Increase in documentation to give theprovider[19,47,73]

3

3%

Increase in satisfaction[4,5,53]

3

3%

Increase in hospital metrics

[26]

2

2%

Increase in guilt

[60]

1

1%

Barriers
Fourteen themes and one observation that did not fit into a theme
for barriers were observed. Table 6 lists the barriers with theirassociated references, number of occurrences, and probabilityof occurrence in this review. The barrier that was reported mostoften was technical literacy (I do not understand technology, I
cannot navigate menus, I do not know how, etc)
[14-16,19,27,28,34,35,37-40,46,47,49,56,58,59,63-65,75]. The
theme noted the second most often was lack of desire (laziness,
I do not want to, I am too busy, etc) [19,28,34,37,
38,49,57,58,60,63,64,67,74,76]. Another theme was cost (tooexpensive, we live off a fixed income, etc) [33,38,
40,48-50,55,58,63,64,67]. The theme lack of technical support
included the following: my friends or family are not able to help
me, I do not understand the interface, etc[14,28,34,58,63-65,73,74]. The theme visual acuity included
the following: fonts or icons are too small, color contrast, etc[4,27,29,37,59,61,73]. The next observation was a surprise to

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our reviewing team; the theme was social implications of using
a telemonitoringdevice (I do not want to bother a first responder,
I do not want a stranger coming to my house, I do not wantanyone coming to my house late at night, I had a bad experiencethe last time I used the telemonitoring device, I do not want myneighbor to overhear me using this thing, I do not have my own
email, I do not understand social media, etc)
[14,28,29,49,56,58,64,66]. The next theme was ownership oftechnology (no phone, no computer, no internet access, etc)
[5,19,28,34,38,58,76]. The last set of themes and observations
comprised less than 25% of the observations, and they wereprivacy and security concerns, medical literacy (I do notunderstand terminology, I do not understand test results, etc),
trust of the internet, mental acuity (computers confuse me, theinterface is too complex, I cannot focus for very long, how did
I get to this page? etc), hand-eye coordination (particularly withthose who have Parkinson disease, but not exclusively), auditory
acuity, and computer anxiety.

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Table 6. Affinity matrix for barriers to the use of telehealth by older adults.
Themes of barriers

References

Number of occurrences(N=144)

Probability of occurrence

Technical literacy[14-16,19,27,28,34,35,37-40,46,47,49,56,58,59,63-65,75]

25

17%

Not reported[26,31,32,36,41-45,51-54,62,68-72]

19

13%

Lack of desire[19,28,34,37,38,49,57,58,60,63,64,67,74,76]

15

10%

Cost

[33,38,40,48-50,55,58,63,64,67]

11

8%

Lack of technical support[14,28,34,58,63-65,73,74]

10

7%Visual acuity[4,27,29,37,59,61,73]

10

7%Social implications[14,28,29,49,56,58,64,66]

9

6%

Ownership of technology[5,19,28,34,38,58,76]

8

6%

Privacy and security[5,19,28,34,38,58,76]

8

6%

Medical literacy[5,14,15,35,38,49,56,73]

8

6%

Trust of the internet[5,14-16,30,58]

6

4%

Mental acuity[27,29,38,59,63,73]

6

4%

Hand-eye coordination[37,58,59,73]

4

3%

Auditory acuity[4,50,61]

4

3%Computer anxiety

[65]

1

1%

Interactions Between Observations
There were several interactions worth discussing. We analyzed
the interactions between interventions and barriers. Ten
instances of eHealth interventions were mentioned withtechnical literacy [14-16,30,33,37,40,58,59,65,74]. Eightinstances of eHealth interventions were mentioned with lack oftechnical support [14,28,34,58,59,65,73,74]. There were eight
instances of mHealth interventions associated with technicalliteracy [19,27,28,35,38,39,49,64], but these were hardly
mentioned at all with lack of technical support [28,74]. Theinterventions of mHealth were also associated with the barrierof lack of desire. This occurred six times in the literature[28,38,39,60,64,74]. Contrary to literature on the digital divide,
eHealth and mHealth were only marginally associated withownership of technology, which occurred four [5,28,34,58] andthree times [19,28,38], respectively. Commensurate withliterature on generational trends, both eHealth and mHealthwere associated with privacy and security concerns, which
occurred four [14,15,34,35] and two times [28,64], respectively.
Both eHealth and mHealth were associated with the barrier
medical literacy, which occurred four [5,14,15,73] and threetimes [35,38,39], respectively. Surprisingly, eHealth was
associated with hand-eye coordination, but mHealth was not[37,58,60,73]. Finally, eHealth was associated with lack of trustof the internet, which occurred six times in the literature[5,14-16,30,58].
We also analyzed the interactions between interventions andmedical outcomes. eHealth interventions were associated withan increase in cognitive ability. This interaction occurred seventimes in the literature [28,31,41,50,51,54,69].

Results Summary
This review identified 13 themes and one lone observation of
medical outcomes incident with the adoption of five types of
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telehealth approaches. This review also identified 14 themes
and one observation of barriers to the adoption of telehealth.

Discussion
Common Barriers to TelehealthIn this review, we were able to identify the common barriers
associated with older adults utilizing telehealth. The mostfrequent barriers were lack of desire, cost, lack of technicalsupport, visual acuity, social implications of use, ownership oftechnology, privacy and security, medical literacy, trust of theinternet, mental acuity, hand-eye coordination, auditory acuity,and computer anxiety. Each of these barrier areas could present
hurdles for elderly people dealing with telehealth and reasons
to not use it. Lack of technical literacy is a large area of concern,as many elderly people have issues using computers to checkemail or smartphones to make telephone calls [13]. Because
this is new to this population, they are also being held back fromacceptance by a simple lack of wanting to do it[28,34,37-39,57,58,60,63,64,67,74,76]. It seems to be an easy
thing to add to one’s daily tasks, but when one has lived largely
without the use of these technologies, it can become an arduous
task to “sell” the benefits of the sudden use of new technology
and learning how to use new technology. They have the attitude“as it was not needed before, why bother to learn it now?” Thiscan prove to be an uphill battle for providers who are attempting
to utilize new technologies in different ways.
The cost of technology is also quite prohibitive, as computers,
smartphones, and other devices cost hundreds to thousands ofdollars. Those living on fixed incomes are cash strapped andmay not be able to afford to purchase or use such newtechnologies. Not owning such technologies presents its own
concerns for the provision of care. Besides cost, there areconcerns in this population regarding the ability to actually
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JMIR MEDICAL INFORMATICS
utilize the modality of telehealth efficiently. Issues with visualacuity [4,27,29,37,59,61,73], mental acuity [27,29,38,59,63,73],
hand-eye coordination [37,58,59,73], and auditory acuity[4,50,61] are all relevant concerns for elderly people. Many
people, as they become older, experience decreases in theefficiencies of the operations of many body systems, including
their senses. Many develop disease processes that can affecttheir mental status, vision, and hearing, and any or all of these
could easily lead to problems with being able to use technology,
let alone having a clear understanding of what they need to be
doing with the device or even how to interact with it.
The elderly population also has relevant concerns with trust andtechnology, as they are one of the prime targets for abuse fromtheir use of technology according to popular media [13,78].
This is where lack of technical support for the use of technology
can become a very relevant area of concern. There is noaffordable and adequate source of “technical support” to simply
learn how to use devices [14,28,59,73]. This lack of knowledge
and available education can be a very problematic barrier for
the use of the modality of telehealth. Furthermore, problems
surrounding trust of the internet [5,14-16,30,58], concerns ofprivacy and security [5,19,28,34,38,58,76], and even computer
anxiety [65] can figure into the use of technology. As there areconcerns with privacy and security, telehealth could easily cause
patients to succumb to some level of anxiety. Not understanding
the modality of telehealth or how to use it can add to the levelof this anxiety at an exponential rate.
Another consideration with the use of telehealth is that it
requires a certain level of user knowledge. The utilization of
medical applications requires the user to have some knowledgeof medical terms, procedures, etc [5,14,15,35,38,49,56,73]. This
is often not the case, as this population was raised without theinternet or medical knowledge. Medical knowledge came fromphysicians during their younger years, and only recently, theapproach has changed to the utilization of internet web searches
to garner knowledge about symptoms and diagnoses. This is anentirely new world for the elderly population and a relevantbarrier to the use of these applications overall. Overcoming thisknowledge gap could prove to be an insurmountable task or one
that requires any telehealth use to be kept to an absolute
minimum for knowledge or know-how on the part of the user.

Common Outcomes Associated With TelehealthInterventions
The research supports strong medical outcomes incident to theuse of telehealth as follows: decreased psychological distress[32,42,46,51,60,68,70,71,76],
increasedautonomy[29,34,35,46,48,52-54,56,62,67,75,76], increased cognitive
ability [27,28,31,41,50,51,54,69], and many others. This review
supports an increased quality of life for those who adopt
telehealth [29,32,44,53,67,69]. The use of telehealth can lead
to less psychological distress, as users know that they have a
way of communicating their medical concerns to their providers
in a much easier and faster way. This could eventually enable
better health due to better management, thus allowing for fewer
associated medical conditions for those patients who use
telehealth for assistance in the management of their care.

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The observation of greater documentation for providers
demonstrated that the use of telehealth is not all about thepatient. It is just as much about practitioners providing care.
The use of telehealth allows for much faster accessibility to
documentation to provide care or even real-time information
about the patient to allow for immediate diagnosis orintervention, based on information being gathered by the usedtechnology. This can make the provision of care easier and much
more efficient for the field, which is already seeing more patients
than it can comfortably manage.

Interactions Among Outcomes, Barriers, and Typesof Interventions
eHealth interventions were the most frequently observed
interventions in the literature, and these interventions were mostfrequently associated with the barriers of technical literacy andlack of technical support. This observation is interesting because
general technical support, whether from friends, neighbors,family, or caregivers, or professionally acquired technicalsupport is a control for the barrier of technical literacy. Theinteraction between eHealth and technical literacy is interesting
as well. This could signal that older adults are more adept at
mobile technology than computer technology for applicationof telehealth. This supposition is supported by the literature
because many older adults are turning to mobile technology tocommunicate with children and grandchildren [13]. Theinteraction between mHealth and lack of desire is noteworthy.
This seems to indicate that older adults are willing to interact
with mobile technology to communicate with children andgrandchildren, but they are not as willing to use it for telehealthinterventions.

Study Quality and Literature Bias
The assessment of the quality of the articles studied is worthyof discussion. The majority (27/57, 49%) of the articles analyzed
were level III (nonexperimental, qualitative, or meta-synthesis
studies). The reviewers would have preferred to analyze only
the highest level (level I) (experimental study or RCT), but only
10 (17%) such studies were available. Fortunately, 98% (56/57)
of the articles were rated as quality level A (high quality) or B
(good quality). The importance of this rating cannot be
understated. If the findings from this review were from
low-quality articles, the results would not be as strong. By
analyzing high-quality articles with strong levels of evidence,
readers can be more assured of the results. Research articles
with strong study designs and sufficiently large samples are
generally accepted in the scientific field for their veracity.

Limitations
The authors identified the low number of articles analyzed asa limitation of this systematic review. If the authors conduct
another systematic review on the same topic, they would liketo have a larger analysis pool. This could be achieved bybroadening the years of study in the selection or by reducing
the threshold of quality. However, the additional years of study
would only repeat the results from previously published reviewsof a similar topic, and lowering the threshold of quality would
introduce articles with dubious results.

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JMIR MEDICAL INFORMATICS
Although not intentional, the authors realized that selection bias
may be present in this article. To combat selection bias, theauthors worked to minimize its effects by ensuring each article
was reviewed by at least two authors. The authors held
consensus meetings after each screening to provide feedback
and reach total agreement on the inclusion and exclusion ofarticles for the analysis.
Another source of bias that could have affected this article ispublication bias. To control for publication bias, the authors
searched the Boolean search string in Google Scholar. Thisaction was intended to identify articles from lesser-known
journals that may not have appeared in MEDLINE or CINAHL.
Another limitation is our inclusion of people aged 50 years orabove in the study of older adults. Most studies categorize older
adults as those aged 65 years or above. The elderly population
currently spans baby boomers and the silent generation. The
youngest members of the former group are still working andare most likely using technology fluently. It is possible that ourgeneralizations do not apply to all members of the elderly
demographic.

Future Research
Health care systems can utilize knowledge of these barriers todevelop solutions for broadening the use of telehealth amongolder adults. A multidisciplinary approach and culture of

Kruse et alcollaboration between administrative leadership and providers
may be the most effective and immediate manner of
implementing solutions to breach these barriers and strengthen
the reach of health care services. However, some barriers may
be out of the scope of impact, and policy makers should consider
supporting the efforts. Future research should be conducted on
methods for personalizing telehealth in older adults before
implementation.

Conclusion
Providing sufficient health care access to the rapidly growing
aging population has been an imminent issue, and telehealth isa useful tool that can provide a solution. While health caresystems increase their telehealth efforts to improve access tohealth care services among vulnerable populations, such as older
adults, some health care organizations do not consider thetechnological, educational, financial, and behavioral barriers
before implementing telehealth solutions. It is imperative that
health care systems use a multidisciplinary approach andcollaborate with health care providers, community partners, andpolicy makers to address these barriers of utilizing telehealthamong older adults and to successfully implement telehealthsolutions. This systematic review provides some understandingof older adults’ perspectives and experiences with the barriersof implementing telehealth services.

Authors' Contributions
Conceptualization: CSK, NW, JF, ENP, and SZ; methodology: CSK, NW, JF, ENP, and SZ; formal analysis: CSK, NW, JF,
ENP, SZ, and MM; investigation: CSK, NW, JF ENP, and SZ; writing—original draft preparation: NW, JF, ENP, and SZ;
writing—review and editing: JF; visualization: ENP; supervision: NW; project administration: SZ; final draft: CSK and MM.

Conflicts of Interest
None declared.

Multimedia Appendix 1
Detailed observations on interventions and corresponding themes.
[DOCX File , 29 KB-Multimedia Appendix 1]

Multimedia Appendix 2
Detailed observations on medical outcomes and corresponding themes.
[DOCX File , 42 KB-Multimedia Appendix 2]

Multimedia Appendix 3
Detailed observations on barriers and corresponding themes.
[DOCX File , 37 KB-Multimedia Appendix 3]

Multimedia Appendix 4Bias, country of origin, statistics, and quality assessments.
[DOCX File , 51 KB-Multimedia Appendix 4]

References1.
2.

Ageing and health. World Health Organization. 2018 Feb 05. URL: https://www.who.int/news-room/fact-sheets/detail/ageing-and-health [accessed 2020-04-02]
From Pyramid to Pillar: A Century of Change, Population of the U.S. US Census Bureau. 2018 Mar 13. URL: https://www.
census.gov/library/visualizations/2018/comm/century-of-change.html [accessed 2020-04-02]

http://medinform.jmir.org/2020/8/e20359/

XSL• FO
RenderX

JMIR Med Inform 2020 | vol. 8 | iss. 8 | e20359 | p. 20(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
3.
4.

5.

6.

7.
8.
9.

10.
11.

12.

13.
14.

15.

16.
17.
18.

19.

20.
21.
22.
23.
24.
25.

Colby S, Ortman J. The Baby Boom Cohort in the United States: 2012-2060. US Census Bureau. 2014 May 01. URL:
https://www.census.gov/library/publications/2014/demo/p25-1141.html [accessed 2020-04-02]
Dham P, Gupta N, Alexander J, Black W, Rajji T, Skinner E. Community based telepsychiatry service for older adults
residing in a rural and remote region- utilization pattern and satisfaction among stakeholders. BMC Psychiatry 2018 Sep
27;18(1):316 [FREE Full text] [doi: 10.1186/s12888-018-1896-3] [Medline: 30261845]
Theis S, Schäfer D, Bröhl C, Schäfer K, Rasche P, Wille M, et al. Predicting technology usage by health information needof older adults: Implications for eHealth technology. Work 2019;62(3):443-457. [doi: 10.3233/WOR-192878] [Medline:
30909259]
Ryu S. Telemedicine: Opportunities and Developments in Member States: Report on the Second Global Survey on eHealth2009 (Global Observatory for eHealth Series, Volume 2). Healthc Inform Res 2012;18(2):153. [doi:10.4258/hir.2012.18.2.153]
Yoon H, Jang Y, Vaughan PW, Garcia M. Older Adults' Internet Use for Health Information: Digital Divide by Race/Ethnicity
and Socioeconomic Status. J Appl Gerontol 2020 Jan;39(1):105-110. [doi: 10.1177/0733464818770772] [Medline: 29661052]
Dorsey ER, Topol EJ. Telemedicine 2020 and the next decade. The Lancet 2020 Mar;395(10227):859. [doi:10.1016/s0140-6736(20)30424-4]
Trump Administration Issues Second Round of Sweeping Changes to Support U.S. Healthcare System During COVID-19
Pandemic. Centers for Medicare & Medicaid Services. 2020 Apr 30. URL: https://www.cms.gov/newsroom/press-releases/
trump-administration-issues-second-round-sweeping-changes-support-us-healthcare-system-during-covid [accessed2020-04-30]
Foster MV, Sethares KA. Facilitators and barriers to the adoption of telehealth in older adults: an integrative review. Comput
Inform Nurs 2014 Nov;32(11):523-33; quiz 534. [doi: 10.1097/CIN.0000000000000105] [Medline: 25251862]
Kampmeijer R, Pavlova M, Tambor M, Golinowska S, Groot W. The use of e-health and m-health tools in health promotion
and primary prevention among older adults: a systematic literature review. BMC Health Serv Res 2016 Sep 05;16 Suppl
5:290 [FREE Full text] [doi: 10.1186/s12913-016-1522-3] [Medline: 27608677]
Kruse CS, Krowski N, Rodriguez B, Tran L, Vela J, Brooks M. Telehealth and patient satisfaction: a systematic review
and narrative analysis. BMJ Open 2017 Aug 03;7(8):e016242 [FREE Full text] [doi: 10.1136/bmjopen-2017-016242][Medline: 28775188]
Kruse CS, Mileski M, Moreno J. Mobile health solutions for the aging population: A systematic narrative analysis. J Telemed
Telecare 2017 May;23(4):439-451. [doi: 10.1177/1357633X16649790] [Medline: 27255207]
Ware P, Bartlett SJ, Paré G, Symeonidis I, Tannenbaum C, Bartlett G, et al. Using eHealth Technologies: Interests,
Preferences, and Concerns of Older Adults. Interact J Med Res 2017 Mar 23;6(1):e3 [FREE Full text] [doi: 10.2196/ijmr.4447][Medline: 28336506]
Rasche P, Wille M, Bröhl C, Theis S, Schäfer K, Knobe M, et al. Prevalence of Health App Use Among Older Adults inGermany: National Survey. JMIR Mhealth Uhealth 2018 Jan 23;6(1):e26 [FREE Full text] [doi: 10.2196/mhealth.8619][Medline: 29362211]
Paige SR, Miller MD, Krieger JL, Stellefson M, Cheong J. Electronic Health Literacy Across the Lifespan: Measurement
Invariance Study. J Med Internet Res 2018 Jul 09;20(7):e10434 [FREE Full text] [doi: 10.2196/10434] [Medline: 29986848]
Choi N. Telehealth Depression Treatments for Older Adults. ClinicalTrials.gov. 2015 Nov 09. URL: https://clinicaltrials.
gov/ct2/show/NCT02600754 [accessed 2020-04-02]
Scogin F, Lichstein K, DiNapoli EA, Woosley J, Thomas SJ, LaRocca MA, et al. Effects of Integrated Telehealth-Delivered
Cognitive-Behavioral Therapy for Depression and Insomnia in Rural Older Adults. J Psychother Integr 2018
Sep;28(3):292-309 [FREE Full text] [doi: 10.1037/int0000121] [Medline: 30930607]
Evans J, Papadopoulos A, Silvers CT, Charness N, Boot WR, Schlachta-Fairchild L, et al. Remote Health Monitoring forOlder Adults and Those with Heart Failure: Adherence and System Usability. Telemed J E Health 2016 Jun;22(6):480-488[FREE Full text] [doi: 10.1089/tmj.2015.0140] [Medline: 26540369]
Kruse CS. Writing a Systematic Review for Publication in a Health-Related Degree Program. JMIR Res Protoc 2019 Oct
14;8(10):e15490 [FREE Full text] [doi: 10.2196/15490] [Medline: 31527018]
Moher D, Liberati A, Tetzlaff J, Altman DG. Preferred reporting items for systematic reviews and meta-analyses: thePRISMA statement. BMJ 2009 Jul 21;339:b2535. [doi: 10.1136/bmj.b2535] [Medline: 19621072]
Braun V, Clarke V. Using thematic analysis in psychology. Qualitative Research in Psychology 2006 Jan;3(2):77-101.
[doi: 10.1191/1478088706qp063oa]
Dang D, Dearholt S. Johns Hopkins Nursing Evidence-based Practice: Model and Guidelines. Indianapolis, IN: SIGMA
Theta Tau International; 2018.
Light RJ. Measures of response agreement for qualitative data: Some generalizations and alternatives. Psychological Bulletin
1971;76(5):365-377. [doi: 10.1037/h0031643]
McHugh ML. Interrater reliability: the kappa statistic. Biochem Med (Zagreb) 2012;22(3):276-282 [FREE Full text][Medline: 23092060]

http://medinform.jmir.org/2020/8/e20359/

XSL• FO
RenderX

Kruse et al

JMIR Med Inform 2020 | vol. 8 | iss. 8 | e20359 | p. 21(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
26.

27.

28.

29.
30.

31.

32.

33.
34.

35.

36.

37.
38.

39.

40.

41.

42.

43.

44.

45.

Hamilton T, Johnson L, Quinn BT, Coppola J, Sachs D, Migliaccio J, et al. Telehealth Intervention Programs for Seniors:
An Observational Study of a Community-Embedded Health Monitoring Initiative. Telemed J E Health 2020
Apr;26(4):438-445. [doi: 10.1089/tmj.2018.0248] [Medline: 30994409]
Wildenbos GA, Jaspers MW, Schijven MP, Dusseljee-Peute LW. Mobile health for older adult patients: Using an aging
barriers framework to classify usability problems. Int J Med Inform 2019 Apr;124:68-77. [doi:10.1016/j.ijmedinf.2019.01.006] [Medline: 30784429]
Jakobsson E, Nygård L, Kottorp A, Malinowsky C. Experiences from using eHealth in contact with health care amongolder adults with cognitive impairment. Scand J Caring Sci 2019 Jun;33(2):380-389. [doi: 10.1111/scs.12634] [Medline:
30628736]
Karlsen C, Moe CE, Haraldstad K, Thygesen E. Caring by telecare? A hermeneutic study of experiences among older adults
and their family caregivers. J Clin Nurs 2019 Apr;28(7-8):1300-1313. [doi: 10.1111/jocn.14744] [Medline: 30552788]
Coley N, Rosenberg A, van Middelaar T, Soulier A, Barbera M, Guillemont J, MIND-AD, HATICE groups. Older Adults'
Reasons for Participating in an eHealth Prevention Trial: A Cross-Country, Mixed-Methods Comparison. J Am Med Dir
Assoc 2019 Jul;20(7):843-849.e5. [doi: 10.1016/j.jamda.2018.10.019] [Medline: 30541689]
Giesbrecht EM, Miller WC. Effect of an mHealth Wheelchair Skills Training Program for Older Adults: A Feasibility
Randomized Controlled Trial. Arch Phys Med Rehabil 2019 Nov;100(11):2159-2166. [doi: 10.1016/j.apmr.2019.06.010][Medline: 31336101]
Brodbeck J, Berger T, Biesold N, Rockstroh F, Znoj HJ. Evaluation of a guided internet-based self-help intervention for
older adults after spousal bereavement or separation/divorce: A randomised controlled trial. J Affect Disord 2019 Jun
01;252:440-449. [doi: 10.1016/j.jad.2019.04.008] [Medline: 31003114]
Mosley CL, Langley LM, Davis A, McMahon CM, Tremblay KL. Reliability of the Home Hearing Test: Implications for
Public Health. J Am Acad Audiol 2019 Mar;30(3):208-216 [FREE Full text] [doi: 10.3766/jaaa.17092] [Medline: 30461396]
Jensen CM, Overgaard S, Wiil UK, Clemensen J. Can Tele-Health Support Self-Care and Empowerment? A Qualitative
Study of Hip Fracture Patients’ Experiences With Testing an “App”. SAGE Open Nursing 2019 Feb 21;5:237796081982575[FREE Full text] [doi: 10.1177/2377960819825752]
Portz JD, Vehovec A, Dolansky MA, Levin JB, Bull S, Boxer R. The Development and Acceptability of a Mobile Application
for Tracking Symptoms of Heart Failure Among Older Adults. Telemed J E Health 2018 Feb;24(2):161-165 [FREE Fulltext] [doi: 10.1089/tmj.2017.0036] [Medline: 28696832]
Castro Sweet CM, Chiguluri V, Gumpina R, Abbott P, Madero EN, Payne M, et al. Outcomes of a Digital Health Program
With Human Coaching for Diabetes Risk Reduction in a Medicare Population. J Aging Health 2018 Jun;30(5):692-710[FREE Full text] [doi: 10.1177/0898264316688791] [Medline: 28553807]
Joe J, Hall A, Chi N, Thompson H, Demiris G. IT-based wellness tools for older adults: Design concepts and feedback.
Inform Health Soc Care 2018 Mar;43(2):142-158. [doi: 10.1080/17538157.2017.1290637] [Medline: 28350186]
Cajita MI, Hodgson NA, Lam KW, Yoo S, Han H. Facilitators of and Barriers to mHealth Adoption in Older Adults With
Heart Failure. Comput Inform Nurs 2018 Aug;36(8):376-382 [FREE Full text] [doi: 10.1097/CIN.0000000000000442][Medline: 29742549]
Harte R, Hall T, Glynn L, Rodríguez-Molinero A, Scharf T, Quinlan LR, et al. Enhancing Home Health Mobile Phone App
Usability Through General Smartphone Training: Usability and Learnability Case Study. JMIR Hum Factors 2018 Apr
26;5(2):e18 [FREE Full text] [doi: 10.2196/humanfactors.7718] [Medline: 29699969]
Gordon NP, Hornbrook MC. Older adults' readiness to engage with eHealth patient education and self-care resources: across-sectional survey. BMC Health Serv Res 2018 Mar 27;18(1):220 [FREE Full text] [doi: 10.1186/s12913-018-2986-0][Medline: 29587721]
Bao T, Carender WJ, Kinnaird C, Barone VJ, Peethambaran G, Whitney SL, et al. Effects of long-term balance training
with vibrotactile sensory augmentation among community-dwelling healthy older adults: a randomized preliminary study.
J Neuroeng Rehabil 2018 Jan 18;15(1):5 [FREE Full text] [doi: 10.1186/s12984-017-0339-6] [Medline: 29347946]
Egede LE, Walker RJ, Payne EH, Knapp RG, Acierno R, Frueh BC. Effect of psychotherapy for depression via hometelehealth on glycemic control in adults with type 2 diabetes: Subgroup analysis of a randomized clinical trial. J Telemed
Telecare 2018 Oct;24(9):596-602. [doi: 10.1177/1357633X17730419] [Medline: 28945160]
Platts-Mills TF, Hollowell AG, Burke GF, Zimmerman S, Dayaa JA, Quigley BR, et al. Randomized controlled pilot studyof an educational video plus telecare for the early outpatient management of musculoskeletal pain among older emergency
department patients. Trials 2018 Jan 05;19(1):10 [FREE Full text] [doi: 10.1186/s13063-017-2403-8] [Medline: 29304831]
Lopez-Villegas A, Catalan-Matamoros D, Lopez-Liria R, Enebakk T, Thunhaug H, Lappegård KT. Health-related qualityof life on tele-monitoring for users with pacemakers 6 months after implant: the NORDLAND study, a randomized trial.
BMC Geriatr 2018 Sep 21;18(1):223 [FREE Full text] [doi: 10.1186/s12877-018-0911-3] [Medline: 30241511]
Dugas M, Crowley K, Gao GG, Xu T, Agarwal R, Kruglanski AW, et al. Individual differences in regulatory mode moderate
the effectiveness of a pilot mHealth trial for diabetes management among older veterans. PLoS One 2018;13(3):e0192807[FREE Full text] [doi: 10.1371/journal.pone.0192807] [Medline: 29513683]

http://medinform.jmir.org/2020/8/e20359/

XSL• FO
RenderX

Kruse et al

JMIR Med Inform 2020 | vol. 8 | iss. 8 | e20359 | p. 22(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
46.

47.

48.
49.
50.

51.

52.

53.

54.

55.

56.
57.
58.

59.
60.

61.

62.
63.
64.
65.
66.

67.
68.

Nalder E, Marziali E, Dawson DR, Murphy K. Delivering cognitive behavioural interventions in an internet-based healthcaredelivery environment. British Journal of Occupational Therapy 2018 Mar 23;81(10):591-600. [doi:10.1177/0308022618760786]
Buck H, Pinter A, Poole E, Boehmer J, Foy A, Black S, et al. Evaluating the older adult experience of a web-based,
tablet-delivered heart failure self-care program using gerontechnology principles. Geriatr Nurs 2017;38(6):537-541. [doi:10.1016/j.gerinurse.2017.04.001] [Medline: 28554497]
Chang C, Lee T, Mills ME. Experience of Home Telehealth Technology in Older Patients With Diabetes. Comput Inform
Nurs 2017 Oct;35(10):530-537. [doi: 10.1097/CIN.0000000000000341] [Medline: 28291156]
Cajita MI, Hodgson NA, Budhathoki C, Han H. Intention to Use mHealth in Older Adults With Heart Failure. J Cardiovasc
Nurs 2017;32(6):E1-E7 [FREE Full text] [doi: 10.1097/JCN.0000000000000401] [Medline: 28248747]
LaMonica HM, English A, Hickie IB, Ip J, Ireland C, West S, et al. Examining Internet and eHealth Practices and Preferences:
Survey Study of Australian Older Adults With Subjective Memory Complaints, Mild Cognitive Impairment, or Dementia.
J Med Internet Res 2017 Oct 25;19(10):e358 [FREE Full text] [doi: 10.2196/jmir.7981] [Medline: 29070481]
Bahar-Fuchs A, Webb S, Bartsch L, Clare L, Rebok G, Cherbuin N, et al. Tailored and Adaptive Computerized Cognitive
Training in Older Adults at Risk for Dementia: A Randomized Controlled Trial. J Alzheimers Dis 2017;60(3):889-911.
[doi: 10.3233/JAD-170404] [Medline: 28922158]
Nahm E, Resnick B, Brown C, Zhu S, Magaziner J, Bellantoni M, et al. The Effects of an Online Theory-Based Bone
Health Program for Older Adults. J Appl Gerontol 2017 Sep;36(9):1117-1144 [FREE Full text] [doi:10.1177/0733464815617284] [Medline: 26675352]
Knaevelsrud C, Böttche M, Pietrzak RH, Freyberger HJ, Kuwert P. Efficacy and Feasibility of a Therapist-Guided
Internet-Based Intervention for Older Persons with Childhood Traumatization: A Randomized Controlled Trial. Am J
Geriatr Psychiatry 2017 Aug;25(8):878-888. [doi: 10.1016/j.jagp.2017.02.024] [Medline: 28365000]
Reijnders JS, Geusgens CA, Ponds RW, van Boxtel MP. "Keep your brain fit!" Effectiveness of a psychoeducational
intervention on cognitive functioning in healthy adults: A randomised controlled trial. Neuropsychol Rehabil 2017
Jun;27(4):455-471. [doi: 10.1080/09602011.2015.1090458] [Medline: 26414279]
Mageroski A, Alsadoon A, Prasad P, Pham L, Elchouemi A. Impact of wireless communications technologies on elder
people healthcare: Smart home in Australia. 2016 Presented at: 13th International Joint Conference on Compouter Scienceand Software Engineering (JCSSE); 2016; Khon Kaen, Thailand p. 1-6. [doi: 10.1109/jcsse.2016.7748862]
Hamblin K, Yeandle S, Fry G. Researching telecare: the importance of context. Journal of Enabling Technologies 2017
Sep 18;11(3):75-84. [doi: 10.1108/jet-04-2017-0016]
Wang J, Carroll D, Peck M, Myneni S, Gong Y. Mobile and Wearable Technology Needs for Aging in Place: Perspectives
from Older Adults and Their Caregivers and Providers. Stud Health Technol Inform 2016;225:486-490. [Medline: 27332248]
Gordon NP, Hornbrook MC. Differences in Access to and Preferences for Using Patient Portals and Other eHealthTechnologies Based on Race, Ethnicity, and Age: A Database and Survey Study of Seniors in a Large Health Plan. J MedInternet Res 2016 Mar 04;18(3):e50 [FREE Full text] [doi: 10.2196/jmir.5105] [Medline: 26944212]
Williams K, Pennathur P, Bossen A, Gloeckner A. Adapting Telemonitoring Technology Use for Older Adults: A Pilot
Study. Res Gerontol Nurs 2016;9(1):17-23 [FREE Full text] [doi: 10.3928/19404921-20150522-01] [Medline: 26020575]
Müller AM, Khoo S, Morris T. Text Messaging for Exercise Promotion in Older Adults From an Upper-Middle-Income
Country: Randomized Controlled Trial. J Med Internet Res 2016 Jan 07;18(1):e5 [FREE Full text] [doi: 10.2196/jmir.5235][Medline: 26742999]
Quinn CC, Shardell MD, Terrin ML, Barr EA, Park D, Shaikh F, et al. Mobile Diabetes Intervention for Glycemic Control
in 45- to 64-Year-Old Persons With Type 2 Diabetes. J Appl Gerontol 2016 Feb;35(2):227-243. [doi:10.1177/0733464814542611] [Medline: 25098253]
Royackers A, Regan S, Donelle L. The eShift model of care: informal caregivers’ experience of a new model of home-based
palliative care. Progress in Palliative Care 2016 Apr 13;24(2):84-92. [doi: 10.1179/1743291x15y.0000000006]
Duh ES, Guna J, Pogačnik M, Sodnik J. Applications of Paper and Interactive Prototypes in Designing Telecare Services
for Older Adults. J Med Syst 2016 Apr;40(4):92. [doi: 10.1007/s10916-016-0463-z] [Medline: 26860915]
Depatie A, Bigbee JL. Rural Older Adult Readiness to Adopt Mobile Health Technology: A Descriptive Study. OJRNHC
2015 May 29;15(1):150-184. [doi: 10.14574/ojrnhc.v15i1.346]
Moore AN, Rothpletz AM, Preminger JE. The Effect of Chronological Age on the Acceptance of Internet-Based Hearing
Health Care. Am J Audiol 2015 Sep;24(3):280-283. [doi: 10.1044/2015_AJA-14-0082] [Medline: 26649530]
Currie M, Philip LJ, Roberts A. Attitudes towards the use and acceptance of eHealth technologies: a case study of older
adults living with chronic pain and implications for rural healthcare. BMC Health Serv Res 2015 Apr 16;15:162 [FREEFull text] [doi: 10.1186/s12913-015-0825-0] [Medline: 25888988]
Grant LA, Rockwood T, Stennes L. Client Satisfaction with Telehealth in Assisted Living and Homecare. Telemed J E
Health 2015 Dec;21(12):987-991. [doi: 10.1089/tmj.2014.0218] [Medline: 26126079]
Brenes GA, Danhauer SC, Lyles MF, Hogan PE, Miller ME. Telephone-Delivered Cognitive Behavioral Therapy andTelephone-Delivered Nondirective Supportive Therapy for Rural Older Adults With Generalized Anxiety Disorder: A

http://medinform.jmir.org/2020/8/e20359/

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JMIR Med Inform 2020 | vol. 8 | iss. 8 | e20359 | p. 23(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

69.

70.

71.

72.

73.

74.
75.
76.

77.
78.

Kruse et al

Randomized Clinical Trial. JAMA Psychiatry 2015 Oct;72(10):1012-1020 [FREE Full text] [doi:10.1001/jamapsychiatry.2015.1154] [Medline: 26244854]
Corbett A, Owen A, Hampshire A, Grahn J, Stenton R, Dajani S, et al. The Effect of an Online Cognitive Training Package
in Healthy Older Adults: An Online Randomized Controlled Trial. J Am Med Dir Assoc 2015 Nov 01;16(11):990-997.
[doi: 10.1016/j.jamda.2015.06.014] [Medline: 26543007]
Mavandadi S, Benson A, DiFilippo S, Streim JE, Oslin D. A Telephone-Based Program to Provide Symptom Monitoring
Alone vs Symptom Monitoring Plus Care Management for Late-Life Depression and Anxiety: A Randomized Clinical
Trial. JAMA Psychiatry 2015 Dec;72(12):1211-1218. [doi: 10.1001/jamapsychiatry.2015.2157] [Medline: 26558530]
Egede LE, Acierno R, Knapp RG, Lejuez C, Hernandez-Tejada M, Payne EH, et al. Psychotherapy for depression in older
veterans via telemedicine: a randomised, open-label, non-inferiority trial. Lancet Psychiatry 2015 Aug;2(8):693-701. [doi:10.1016/S2215-0366(15)00122-4] [Medline: 26249300]
Chang W, Hou CJ, Wei S, Tsai J, Chen Y, Chen M, et al. Utilization and Clinical Feasibility of a Handheld Remote
Electrocardiography Recording Device in Cardiac Arrhythmias and Atrial Fibrillation: A Pilot Study. International Journalof Gerontology 2015 Dec;9(4):206-210. [doi: 10.1016/j.ijge.2015.06.002]
Boulos M, Ifeachor E, Zhao P, Escudero J, Carroll C, Costa P, et al. LiveWell-Promoting Healthy Living and Wellbeing
for Parkinson Patients through Social Network and ICT Training: Lessons Learnt and Best Practices. International Journalof Healthcare Information Systems and Informatics 2015;10(3):24-41. [doi: 10.4018/IJHISI.2015070102]
Diño MJ, de Guzman AB. Using Partial Least Squares (PLS) in Predicting Behavioral Intention for Telehealth Use among
Filipino Elderly. Educational Gerontology 2014 Jun 25;41(1):53-68. [doi: 10.1080/03601277.2014.917236]
Czaja SJ, Lee CC, Arana N, Nair SN, Sharit J. Use of a telehealth system by older adults with hypertension. J Telemed
Telecare 2014 Jun;20(4):184-191 [FREE Full text] [doi: 10.1177/1357633X14533889] [Medline: 24803275]
Choi NG, Wilson NL, Sirrianni L, Marinucci ML, Hegel MT. Acceptance of home-based telehealth problem-solving therapy
for depressed, low-income homebound older adults: qualitative interviews with the participants and aging-service casemanagers. Gerontologist 2014 Aug;54(4):704-713 [FREE Full text] [doi: 10.1093/geront/gnt083] [Medline: 23929664]
Chi N, Demiris G. A systematic review of telehealth tools and interventions to support family caregivers. J Telemed Telecare2015 Jan;21(1):37-44 [FREE Full text] [doi: 10.1177/1357633X14562734] [Medline: 25475220]
Knowles B, Hanson V. Older Adults’ Deployment of ‘Distrust’. ACM Trans. Comput.-Hum. Interact 2018 Sep 15;25(4):1-25.
[doi: 10.1145/3196490]

Abbreviations
JHNEBP: John Hopkins Nursing Evidence-Based Practice
MeSH: Medical Subject Headings
mHealth: mobile health
RCT: randomized controlled trial
WHO: World Health Organization

Edited by J Hefner, G Eysenbach; submitted 17.05.20; peer-reviewed by S MacEwan, J Portz; comments to author 08.06.20; revised
version received 11.06.20; accepted 06.07.20; published 12.08.20
Please cite as:
Kruse C, Fohn J, Wilson N, Nunez Patlan E, Zipp S, Mileski M
Utilization Barriers and Medical Outcomes Commensurate With the Use of Telehealth Among Older Adults: Systematic Review
JMIR Med Inform 2020;8(8):e20359
URL: http://medinform.jmir.org/2020/8/e20359/
doi: 10.2196/20359
PMID: 32784177©Clemens Kruse, Joanna Fohn, Nakia Wilson, Evangelina Nunez Patlan, Stephanie Zipp, Michael Mileski. Originally publishedin JMIR Medical Informatics (http://medinform.jmir.org), 12.08.2020. This is an open-access article distributed under the termsof the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly
cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as thiscopyright and license information must be included.

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