eHealth for Addressing Balance Disorders in the Elderly: Systematic Review

Background: The population is aging on a global scale, triggering vulnerability for chronic multimorbidity, balance disorders, and falls. Falls with injuries are the main cause of accidental death in the elderly population, representing a relevant public health problem. Balance disorder is a major risk factor for falling and represents one of the most frequent reasons for health care demand. The use of information and communication technologies to support distance healthcare (eHealth) represents an opportunity to improve the access and quality of health care services for the elderly. In recent years, several studies have addressed the potential of eHealth devices to assess the balance and risk of falling of elderly people. Remote rehabilitation has also been explored. However, the clinical applicability of these digital solutions for elderly people with balance disorders remains to be studied. Objective: The aim of this review was to guide the clinical applicability of eHealth devices in providing the screening, assessment, and treatment of elderly people with balance disorders, but without neurological disease. Methods: A systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement. Data were obtained through searching the PubMed, Google Scholar, Embase, and SciELO databases. Only randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published between January 2015 and December 2019 were included. The quality of the evidence to respond to the research question was assessed using Joanna Briggs Institute (JBI) Critical Appraisal for RCTs and the JBI Critical Appraisal Checklist for QESs. RCTs were assessed using the Cochrane risk of bias tool. We provide a narrative synthesis of the main outcomes from the included studies. Results: Among 1030 unduplicated articles retrieved, 21 articles were included in this review. Twelve studies explored different technology devices to obtain data about balance and risk of falling. Nine studies focused on different types of balance exercise training. A wide range of clinical tests, functional scales, classifications of faller participants, sensor-based tasks, intervention protocols, and follow-up times were used. Only one study described the clinical conditions of the participants. Instrumental tests of the inner ear were neither used as the gold-standard test nor performed in pre and postrehabilitation assessments. Conclusions: eHealth has potential for providing additional health care to elderly people with balance disorder and risk of falling. In the included literature, the heterogeneity of populations under study, methodologies, eHealth devices, and time of follow-up did not allow for clear comparison to guide proper clinical applicability. This suggests that more rigorous studies are needed. (J Med Internet Res 2021;23(4):e22215) doi: 10.2196/22215


Aging and Balance Disorders
The improvement of health conditions and the increase in life expectancy have led to an aging global population, although this is not always accompanied by an increase in healthy life years [1][2][3][4].
Aging is associated with functional deterioration, including in the peripheral sensory structures, thereby affecting vision, hearing, and balance [5,6]. Additionally, elderly individuals are more likely to suffer from multiple chronic conditions, which often leads to frailty with risk of falls [1][2][3][4]. Falls in elderly people represent a serious public health problem as the main cause of accidental death in this population. The risk of falling increases with age [1,[7][8][9]. Each year, approximately one in every three elderly people experiences a serious fall. Moreover, falling can lead to deterioration of the quality of life, anxiety, depression, restriction in daily activities, decreased mobility, social isolation, increased consumption of medications, and increased dependence on medical services and informal caregivers [1,2].
Several causes of falls in the elderly population have been identified, including age, environmental factors (eg, wet paths), inappropriate clothing and shoes, incorrect behavior (eg, climbing chairs), excessive alcohol consumption, inadequate use of medications, deteriorating chronic illness, and balance disorders [1,6,10].
Various clinical conditions are associated with balance disorders in elderly people, including age-related decline in balance function (prebyvestibulopathy); medications; and cardiovascular, metabolic, musculoskeletal, neurologic, and otologic diseases [5,6].
Although dizziness and vertigo are recognized as significant factors increasing the risk of falling and are common symptoms among the elderly, epidemiological studies have revealed large variability in the prevalence of balance disorders in this population [11][12][13]. It is estimated that at least 30% of individuals above 60 years old suffer from vertigo and dizziness, increasing to 50% for those above 85 years old [13]. According to the 2008 National Health Interview Survey, 33 million US adults had balance disorders, 26% of whom were elderly people (above 65 years) [14]. Approximately 20% of elderly people in the United States have a balance disorder event annually [15]. In fact, dizziness is a common complaint among the elderly population and is a strong predictor of falling events with a negative impact on quality of life [16]. Poor balance is frequently associated with falling [17,18]. In particular, asymmetrical vestibular function may often contribute to falls and fractures in elderly people [19][20][21].
Balance disorders and consequent falls have progressively represented a burden of disease, accompanied by high costs and pressure on the social services and health care systems related to medical care. This includes repeated consultations, excessive use of diagnostic imaging, and emergency care [22][23][24]. For example, the first national study in the context of dizziness and vertigo in the Emergency Services of United States of America for 2011 revealed that 25.7% of patient complaints of dizziness and vertigo were associated with balance disorders. The cost was estimated at about US $768 per episode, translating to an annual national cost of US $757 million. In the same context, cardiovascular diseases (linked to 16.5% of these episodes) represented a cost of approximately US $1489 per episode for an annual cost of US $941 million. By comparison, cerebrovascular diseases only accounted for 3.1% of these episodes, but with a cost per episode of approximately US $1059 or an annual cost of US $127 million. With the progressive aging of the population, worsening of this situation is expected in the future [25]. Indeed, vertigo is already contributing to the increasing trend of health care costs, which is linked to the aging of the population [23,24].
In this scenario of global aging, the use of digital solutions has been encouraged. Moreover, the additional pressure of the current COVID-19 pandemic has motivated the broader use of eHealth technologies [26].

Digital Health Care and the Elderly
The aging trend represents a relevant challenge to both patients and their families, and to the sustainability of health care systems globally. This is linked to the goal of global health policies for achieving a more active and healthy aging society with autonomy and independence [27,28]. The provision of new health care models, including eHealth services, has been encouraged to tackle access inequities, optimize health outcomes, and ensure autonomy and social support for elderly people. The use of eHealth seems to decrease costs associated with both institutionalization and unnecessary hospital visits [27][28][29]. eHealth consists of the use of information and communication technologies (ICTs) to support a health care communication channel at a distance, allowing for more efficient delivery of care services with optimized resource allocation. eHealth often contributes to improving the quality of health care services, including faster access to health information, promotion of the globalization of health care, and better health outcomes [30]. The World Health Organization has also recommended eHealth to promote universal health coverage, envisaging higher health care services availability with fewer resources and larger patient interaction. To date, eHealth has been used in the management of many conditions from health literacy promotion to teleconsultations [31]. The remote access systems can actively monitor elderly people in a real-life environment, leveraging the fact that there is an increasing interest and engagement of the elderly with technology. Moreover, eHealth technologies can enhance medical-patient interactions and mitigate many care access inequities. However, digital training of elderly people and caregivers is essential [4,[32][33][34].

Assessment and Rehabilitation for the Elderly with Balance Disorders
There are several clinical tests and functional scales, including the Timed Up and Go Test (TUGT), Unipedal Standing Test, and Berg Balance Scale, that allow for assessments of balance, gait, and risk of falling [5,35]. The use of sensors can improve the data quality of these tests and scales [36,37]. Additionally, functional tests of the inner ear, such as videonystagmography or the Video Head Impulse Test, are essential to identify and measure balance disorder cases, including an age-related decline in balance function (prebyvestibulopathy) [6].
Personalized balance training is a relevant option for the treatment of elderly people with balance disorders and risk of falling [5]. This training consists of an exercise-based program to address an individual's specific balance disorder, with goals of increasing postural stability, improving activities of daily living, and decreasing symptoms. Balance training should be focused on the functional deficiencies identified. Therefore, a prior medical evaluation is necessary to identify the clinical conditions related to poor balance as mentioned previously [38][39][40][41]. Moreover, these clinical conditions can affect the outcomes. For example, intervention success is more difficult when the patient has a disorder of both inner ears or has limited mobility due to an osteoarticular disease [38][39][40][41]. Exercises delivered through video games can be a promising intervention to achieve greater access and adherence among elderly people [42,43].
Several reviews have addressed the potential of digital solutions to improve the clinical observation and evaluation of balance disorders, and to promote the remote balance rehabilitation of elderly people [36,37,[42][43][44][45][46][47]. However, most of these reviews included studies using a younger population as a preliminary assessment [42,[44][45][46][47], and the majority did not describe the clinical conditions of the participants that might interfere with the outcomes, especially in the context of balance rehabilitation. Additionally, the clinical applicability of these devices was not assessed [36,37,[42][43][44][45][46][47].
Therefore, there is a gap in this field in terms of evaluating the overall applicability of digital solutions according to the clinical conditions of elderly people with balance disorders and without neurological disease.

Objectives
The aim of this review was to evaluate and guide the clinical applicability of eHealth devices in the screening, assessment, and treatment of elderly people with balance disorders but without neurological disease.

Design
This systematic review was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) statement [48] with the following steps: development of research questions, development of a search strategy with eligibility criteria, data selection, and qualitative analysis.
The protocol for this systematic review was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD42019120774) and the complete protocol is available on the National Institute for Health Research program website.
This review focused on answering the following specific research questions, according to the PICO (Population, patient, or problem; Intervention; Control, Comparison, or Comparator; Outcome) strategy [49]

Definition of Concepts and Keywords used in the Search Strategy
In this study, we defined elderly people as those over 60 years of age [50]. Knudson [51] defined balance as a "person's ability to control their body position relative to some base of support." According to Agrawal et al [6], vertigo and dizziness are defined as "sensation of self-motion when no self-motion is occurring or the sensation of distorted self-motion during an otherwise normal head movement" and "sensation of disturbed or impaired spatial orientation without a false or distorted sense of motion," respectively. Falls refer to "inadvertently landing on the ground, floor or other lower level" [10]. Gait is defined as "the pattern of movement of the body during locomotion" [52].
Telemedicine is defined according to the World Health Organization Group Consultation on Health Telematics [53] as "delivery of health care services using ICT for the exchange of valid information for diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of health care providers." eHealth is defined according to Eysenbach [54] as: Teleconsultation is defined as "synchronous or asynchronous consultation using ICT to omit geographical and functional distance" [55]. Finally, a sensor is defined as a "device that responds to a physical input of interest with a recordable functionally related output that is usually electrical or optical" [56].

Search Strategy
Articles were retrieved through searching the PubMed, Google Scholar, Embase, and SciELO databases. The search algorithm included multiple group combinations, as shown in Table 1. Table 1. Search strategy.

Selection Criteria
The inclusion criteria were randomized controlled trials (RCTs) or quasiexperimental studies (QESs) published in English between January 2015 and December 2019, studies related to use of eHealth in the context of balance and falls, and the sample was restricted to an elderly population (60 years old and above).
The exclusion criteria were: (1) review articles, brief reports, protocols, proof-of-concepts, pilot studies, conference papers, and letters to the editor; (2) studies including elderly people with a reported functional limitation due to a neurological disease; and (3) articles without an age sample reference or with participants aged below 60 years.

Screening Process and Data Extraction
First, both authors screened the papers independently, looking at titles, abstracts, and methods, and agreed about their inclusion or exclusion according to the eligibility criteria. Second, the potentially relevant papers were retrieved for full-text evaluation against the eligibility criteria. Any articles that were deemed to be questionable in the first stage were included for further evaluation in the second stage. The selection of papers was performed by checking the extracted data and risk of bias.

Outcome Measures
The main outcomes included population characteristics, balance disorder, identification of faller participants, eHealth platform and services, health benefits, and fall prevention literacy.

Risk of Bias Assessment
The quality of the evidence to respond to the research questions was independently assessed using the Joanna Briggs Institute (JBI) Critical Appraisal for Experimental Studies and JBI Critical Appraisal Checklist for Quasi-Experimental Studies tools [57]. The two researchers discussed the results of the quality appraisal, reaching a consensus in case of any divergence. The included RCTs were assessed using the Cochrane risk of bias tool [58] to evaluate the risk of internal bias for a series of domains: selection bias, performance bias, detection bias, attrition bias, and reporting bias. Disagreements were solved by consensus between the two researchers.

Data Analysis
We provide a narrative synthesis of the main outcomes from the included studies. First, the articles were categorized according to the study design. Second, the articles were categorized based on the focus of eHealth services (screening/assessment and treatment/rehabilitation) for comparison of clinical use and applicability according to digital devices.

Search Results
A total of 1030 unduplicated articles were identified, 984 of which were excluded after title and abstract screening. Among the 46 full-text publications assessed for eligibility, 25 articles were excluded owing to functional limitations due to neurological disease (n=4), age of participants (n=5), focus on technological implementation (n=2) or model/algorithm (n=8), and specific descriptions of elderly gait parameters (n=6).
Twenty-one articles  were ultimately included in the review ( Figure 1).
The inclusion criteria were mentioned in all articles. However, the clinical conditions of the participants were only described in one study [60]. The function of the inner ear was never mentioned. Thus, the expected similarity between the control and intervention groups was not clear. This is relevant because various clinical conditions (eg, cardiovascular, metabolic, inner ear disease, medication) can interfere with the outcomes of balance rehabilitation [38][39][40][41]. Therefore, the lack of information about clinical conditions of the participants, including the lack of data about function of the inner ear, was considered as "other bias" and was a common weakness of all included RCTs (Table  3 and Figure 2). This approach led to a worse classification of the quality of these studies.
Additionally, the blinding of participants, personnel, and outcome assessment were unclear in some of these studies.
In the control and intervention groups of all RCTs, a few dropouts for medical and personal reasons were mentioned. However, this was not considered to be sufficiently relevant to have an impact on the results. Only two papers reported intention-to-treat analysis [60,63].
All outcomes were measured in a reliable manner and were considered to have been properly analyzed.

QES Design
Fourteen studies were QESs [66][67][68][69][70][71][72][73][74][75][76][77][78][79]; only one of these was a multicenter study [73]. Twelve of these studies used the same group of participants [66,[68][69][70][71][72][73][74][75][76][77]79]. One study used two groups with different participants [67] and another had a control group and an intervention group [78]. However, the expected similarity between the groups was not clear because there was no description of the clinical conditions of the participants, including function of the inner ear. Loss to follow-up was also not mentioned for any of these studies (Table 4).  Multiple different measurements of the outcomes were used (Table 4). However, the instrumental inner ear tests were not used as the gold-standard test. This lack of comparison was considered to be a weakness of all of the included QESs. The most commonly applied tests were the TUGT and walking over different distances. One study assessed 1-week daily-life walking [73]. Only one study explored the activities of daily living [75] (Table 5).
The outcomes were considered to have been measured in a reliable manner and were properly analyzed.
The age range was 60-91 years for the RCTs and 60-92 years for the QESs. Most of the studies included more women than men. In four studies, only women participated [65,67,71,76]. The decision to only recruit women was explained in one study as "to avoid the influence of gender differences on risk of falling" [76]. Two studies excluded the few male participants [67,71] and the remaining article did not describe the reason for the exclusive participation of women [65]. One study did not describe the age range or the gender distribution of the participants [79].

Balance Disorder and Identification of Faller Participants
The included studies used functional balance tests, with or without sensors, to evaluate balance and risk of falling. An objective identification via exploration and quantification of the function of the inner ear by instrumental tests was not employed in any of the considered studies, as mentioned above. Therefore, the presence of prebyvestibulopathy or other balance disorders was not known.
Some authors highlighted the potential of sensor-based tests in identifying early balance deficits [71] and in evaluating the risk of falling [66,72,73,76,77]. Improved balance and gait with technology-based training were mentioned in some studies [59,62,64].
The identification of faller participants based on retrospective [68,70,73,76] or prospective occurrence of falling [74] was employed to compare the technology results. The benefits of virtual training in reducing the risk of falling was also described [60,61,65,78].
No study focusing on detection of falling fully complied with the inclusion criteria of this review (RCTs or QESs, published in English between January 2015 and December 2019, restricted to the population 60 years or older).

eHealth Platform and Services
Different platforms were used for the provision of eHealth services. The main platforms identified were computer-based apps, either via the internet or mobile based platforms (Tables  5 and 6).
Nine studies explored balance rehabilitation [59][60][61][62][63][64][65]67,78] with different exercises and duration of training. The follow-up time was short (less than 6 months) in most of the studies, with the longest follow-up of 1 year [59]. The development of eHealth services was explored both inside and outside the laboratory environment (Table 6).
One study used a robot to provide information about training and fall prevention. However, the authors pointed out that the screen and the volume speaker were not adequate for use by elderly people [78].
The use of technical language and the presence of disabilities such as visual and hearing impairment were highlighted as the main barriers in using eHealth [78].

Health Benefits
Only one study did not report better adherence, enjoyment, motivation, and balance performance with virtual training. This was explained by the possible fragility of the sample included in the study and by the short duration of the training intervention [63].
The remaining papers emphasized the potential contribution of digital solutions to improve balance performance and risk of falling. The sensors used during balance tests improved the evaluation of balance and gait [66,68,69,71,72,79] and improved the identification of potential faller participants [70,[73][74][75][76][77]. In addition, the use of eHealth devices for balance rehabilitation increased balance and gait performance [59,60,62,64,65,78], and reduced the risk of falling [60,61]. However, no long-term follow-up was reported. Virtual programs of falls prevention seemed to increase knowledge on the subject [78] (Table 7).  [66] Sensor-based gait assessment: potential of identification of gait changes QES, screening Howcroft et al [68] Advantages of wearable sensor as an outside laboratory tool QES, screening Lee et al [69] Improved potential of identification of fallers with single sensor-based DT c TUGT QES, screening Ponti et al [70] Sensor-based walk test: a screening tool to identify early signs of balance deficits QES, screening Similä et al [71] Importance of sensor-based TUGT, STS-5 d , and AST e on fall risk estimation QES, screening Shahzad et al [72] Better sensor-based daily-life gait assessment to discriminate fallers QES, multicenter screening Brodie et al [73] Sensor: potential to discriminate differences between ST f and DT gait and between prospective fallers and nonfallers QES, screening Howcroft et al [74] Wearable accelerometer: useful for nonsedentary activity recognition and gait detection in frail older adults outside lab facilities QES, screening Chigateri et al [75] Potential use of wearable inertial sensor-based systems for elderly fall risk assessment QES, screening Qiu et al [76] Validity of smartphone for evaluation of postural stability and fall risk stratification in older adults QES, screening Hiesh et al [77] Using Optogait system: TUGT as a tool for screening balance deficits QES, screening Nightingale et al [79] Virtual reality game dancing with simultaneous cognitive-physical training and treadmill walking with simultaneous verbal memory training: potential to enhance gait variables RCT g , rehabilitation Eggenberger et al [59] iStoppFalls program reduced physiological fall risk and improved postural sway RCT, rehabilitation Gschwind et al [60] Step-mat-training and Microsoft-Kinect exergames reduced fall risks, Step-mattraining improved specific cognitive functions; neither intervention improved balance control RCT, rehabilitation Gschwind et al [61] WiiFit feasible to safely use, Ski Slalom game similar effect as computerized dynamic posturography QES, rehabilitation Hall et al [67] Balance training with biofeedback: most beneficial for the most difficult tasks but with few long-term benefits RCT, rehabilitation Lim et al [62] Superior results of conventional training with respect to adherence, enjoyment, and motivation; no difference of balance during walking between conventional and training with exergames RCT, rehabilitation Oesch et al [63] BEAR h training more effective for improving dynamic balance and lower extremity muscle strength Crossover trial without a washout term, rehabilitation Ozaki et al [64] Telepresence exercise program: effective to improve balance and reduce fear of fall; no significant difference of fall efficacy between intervention (telepresence exercise sessions) and control group (maintained lifestyle) RCT, rehabilitation Hong et al [65] Robotic fall prevention program increased fall prevention knowledge, promoted exercises, and improved balance QES, rehabilitation Maneproom et al [78] a QES: quasiexperimental study.

Fall Prevention Literacy
None of the studies explored the previous health literacy of the participants. Only two papers described the educational level of the participants [60,78].
One study compared use of a fall prevention software to a conventional handbook to evaluate the improvement of knowledge on fall prevention. Both the intervention and control groups showed improvement in knowledge, without a significant difference [78].

Principal Findings
Population aging, and the associated vulnerability to the development of multiple chronic pathologies and balance disorders, have motivated research and the implementation of new strategies for the provision of health care. eHealth devices have been studied to help assess balance and gait performance, risk of falling in and outside a laboratory setting, and to perform in-home balance rehabilitation. In this review, we confirmed the potential of eHealth to complement the health care of elderly people. However, most of these studies were not designed to provide clinical guidelines.
Despite growing interest about this subject in the last 20 years, we decided to focus on studies published in the last 5 years (RCTs and QESs), taking into consideration both continuous advances in technological innovation and the opportunity to apply new clinical applications in balance disorder and risk of falling for the elderly population.
Unlike other reviews, our eligibility criteria ruled out many initially retrieved articles, especially studies with participants under 60 years old, those without reporting the age of participants, or with participants having a functional limitation due to neurologic disease. Therefore, only 21 articles fully complied with the requirements of this review .
Except for one study [63], the others showed the potential of eHealth to evaluate balance assessment and risk of falling of elderly people and to promote balance training. The eHealth devices allowed collecting additional information about the balance, gait, and risk of falling of elderly people, and to monitor their daily activities.
In particular, eHealth seems to provide an opportunity for increasing medical-patient interactions and to reduce access inequities [30]. In 1996, Viierre et al [80] had already mentioned the potential of eHealth in this field: "remote medical diagnosis and treatment facilities could make the few vestibular disorder specialists much more available to patients." However, as observed in other reviews [36,37,[42][43][44][45][46][47], the differences in methodologies and of variables included in the studies did not allow for a proper comparison to guide clinical applicability.
First, there was a broad range of sample sizes, which were generally quite small (ranging from 16 to 196 participants). A small sample of participants is considered a limitation for extrapolating the results, especially for the exploration of risk of falling.
Second, there were missing data about the clinical conditions of the participants. Except for one study [60], several volunteers were recruited from the community and were defined as "healthy" elderly people only based on a self-reported assessment. There were also participants recruited from geriatric services without reference to their clinical conditions. Despite the exclusion of participants with self-reported balance disorders in two studies [62,63], we consider that the exclusion rules should be more rigorous and based on objective data such as instrumental inner ear tests. We have to take into consideration that elderly people can have instability due to many conditions, including the normative aging process, and therefore the outcomes from a balance rehabilitation intervention could be sensitive to these differences [38][39][40][41].
Third, different research methodologies were used for screening and assessment. We observed a wide range of clinical tests, functional scales, faller classifications, and sensor-based tasks among the included studies. The lack of homogeneity of these variables limited an appropriate comparison among the studies. Moreover, functional inner ear tests were not used as the gold-standard test. We consider this as a weakness common to all studies.
Fourth, different types of sensors were used for screening and assessment. Similar to the findings of other reviews [36,37,[44][45][46][47], the studies employed mainly accelerometers, with variations in both number and body location.
Fifth, as observed previously [42,43], studies focusing on treatment and rehabilitation used different devices, training durations, and follow-up times. Some authors employed supervised training. In one study, this was used a telepresence-based exercise platform [65]. Others employed in-home self-regulated exercises training [61], thereby avoiding the need for participants to travel to the rehabilitation center. None of the studies described pre and postintervention data about the function of the inner ear. The studies did not verify the long-term effect of training, especially with respect to fall occurrence. Only two studies explored a sensor used in real-life activities [73,75], which is relevant since it allowed for a better evaluation of the remote interaction and monitoring of daily activities.
Additionally, we observed a constraint related to the use of devices that are not fully adequate to match the abilities of elderly people [78]. We also highlight the importance of providing a better definition of the eHealth user profile to improve adherence.
Future studies in this field should consider the above topics as a starting point, as well as for health policy implementations on eHealth apps for elderly people with balance disorders.
The use of eHealth can play an important role as a complementary method to provide health care services, encouraging health promotion and patient participation, as well as allowing for the remote management of balance disorders.

Recommendations
Based on this review, we can provide the following recommendations to improve studies and applications of eHealth for preventing fall risk in the elderly population.
First, this review highlights the need for further research on the use of eHealth devices in proper clinical settings. This represents an opportunity to be explored, reaching out to elderly people with balance and risk of falling.
Second, despite several efforts to explore balance among the elderly, there is still a need for better characterization and description of the health condition of the population under study.
In particular, we recommend future studies to include the results of functional tests of the inner ear as a gold-standard test or for comparison of the outcome before and after remote balance rehabilitation. Most of the interventions were developed with only functional balance tests. Future studies should also focus on the real-life environment, allowing for additional information of the daily activities among elderly participants.
Third, a longer follow-up time is important to evaluate the long-term benefits of eHealth tools on the balance performance and risk of falling of elderly people.
Finally, the eHealth devices should be user-friendly to improve adherence among elderly people.

Limitations
This review was limited to articles written in the English language and available on the PubMed, Google Scholar, Embase, and SciELO databases for the last 5 years; therefore, it is possible that relevant studies were missed.

Conclusions
The inclusion of eHealth services can play a critical role for the better provision of health care to elderly people with a balance disorder and risk of falling. The differences in populations, methodologies, eHealth devices, and follow-up times of the included studies did not allow for a clear comparison between results, therefore limiting the possibility of obtaining valid guidance for clinical applicability. More rigorous studies are recommended.