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Abstract

Objectives: This systematic review and meta-analysis aimed to investigate the effect of fall prevention interventions using information and communication technology (ICT). Methods: A comprehensive search across four databases was performed. The inclusion criteria were fall prevention interventions including telehealth, computerized balance training, exergaming, mobile application education, virtual reality exercise, and cognitive-behavioral training for community-dwelling adults aged ≥60 years. Results: Thirty-four studies were selected. Telehealth, smart home systems, and exergames reduced the risk of falls (RR = 0.63, 95% CI [0.54, 0.75]). Telehealth and exergame improved balance (MD = 3.30, 95% CI [1.91, 4.68]; MD = 4.40, 95% CI [3.09, 5.71]). Telehealth improved physical function (SMD = 0.69, 95% CI [0.23, 1.16]). Overall, ICT fall interventions improved fall efficacy but not cognitive function. For quality of life (QOL), mixed results were found depending on the assessment tools. Conclusion: Future investigations on telehealth, smart home systems, or exergames are needed to motivate older adults to exercise and prevent falls.

Keywords

accidental falls fall prevention information and communication technology older adults telehealth

Introduction

The global population is progressively aging. It is projected that approximately 1.6 billion people worldwide will be older than 65 years, accounting for 16.7% of the global population of 9.4 billion.¹ Older adults are susceptible to injuries, with falls being the most common cause.² Approximately 30% of adults aged ≥65 years experienced falls at least once per year, and among them, 10.2% reported sustaining fall-related injuries.³ Falling also contributes to injury-related fatalities.⁴ Although most falls are non-fatal, some lead to serious injuries, including hip fractures. Approximately one-third of patients with femoral neck fractures experience declining health and die within a year.⁵ A study estimates that by 2030, there will be 100,000 fatal falls among older adults annually, costing the United States $100 billion.² Numerous fall prevention interventions have been developed to improve the health and safety of older adults. A recent systematic review revealed that comprehensive multi-component programs, which include education, exercise, medication, mobility devices, vision, psychological management, and environmental modifications,⁶ were found to be effective in preventing falls.⁷

The need for non-face-to-face fall interventions has rapidly increased due to the advancement of digital health solutions and the threat posed by the coronavirus disease 2019 (COVID-19) pandemic. Subsequently, various information and communication technology (ICT) interventions for fall prevention have been developed.⁸ ICT interventions can be classified into five categories: (a) telehealth, including exercises conducted in a non-face-to-face setting; (b) exergames that aim to improve physical well-being and leisure; (c) combined exercise and cognitive training; (d) computer feedback on balance control; and (e) smart home systems and fall detectors. Telehealth refers to communicating between healthcare providers and patients at home via phone or video calls.⁹ Exergames are forms of physical activities combining exercises and games, which often use motion-sensing technologies to track a player’s motions. To evaluate the impact of non-face-to-face interventions on fall prevention, it is necessary to assess the effectiveness of each program.

Previous studies have investigated the effects of ICT interventions on fall prevention. However, most of these studies focused on partial ICT interventions, fall monitoring systems, or ICT combined with other interventions. Some studies have focused exclusively on equipment such as fall detection devices or sensors, rather than exploring the broader scope of digital healthcare.^10–13 One review¹⁴ focused on the technologies of fall monitoring systems and not comprehensive fall interventions. Another systematic review¹⁵ examined psychometric properties, including the reliability and validity of balance and fall risk assessment tools. A systematic review¹⁶ analyzed the effects of fall prevention interventions, however, no meta-analysis provided integrated quantitative results. While another recent systematic review⁸ analyzed the effects of ICT interventions, it included non-ICT interventions, such as direct home visits¹⁷ or telephone call.¹⁸ Recently, a network meta-analysis¹⁹ evaluated the effectiveness of technology-assisted rehabilitation on functional mobility, but their primary outcome of interest was not falls. To clarify their effects, it is necessary to analyze the outcome and effectiveness of ICT interventions for fall prevention. In this systematic review and meta-analysis, we aimed to analyze the effectiveness of different types of ICT interventions (e.g., telehealth, computerized balance training, exergaming, mobile application education, or virtual reality exercises) on fall prevention. The primary outcomes of interest were fall rates, proportion of fallers, balance, fall efficacy, and physical function. The secondary outcomes were quality of life, and cognitive function.

Method

This systematic review was conducted following the 2020 PRISMA statement²⁰ and the Cochrane guidelines.

Eligibility criteria

The inclusion criteria were as follows: (a) community-dwelling individuals aged ≥60 years; (b) experimental studies including fall prevention interventions using ICT; and (c) studies written in English. The interventions included telehealth, computerized balance training, exergaming, exercise with virtual reality, education through mobile applications, and cognitive-behavioral training. The outcomes included fall rates, proportion of fallers, balance, fall efficacy, quality of life, physical function, and cognitive function. The exclusion criteria were as follows: (a) posters, abstracts, studies on animals, non-English-language studies, duplicated studies; (b) studies with a mix of online and offline interventions; and (c) protocols and reviews.

Data sources

The selection of the search database was based on the COre, Standard, Ideal (COSI) model endorsed by the National Library of Medicine in the United States, following formal and established protocols.²¹ We searched the following databases for published articles up until November 15, 2023: Ovid-MEDLINE, Ovid-EMBASE, Cochrane Library and Cumulative Index of Nursing and Allied Health Literature (CINAHL).

Search strategy

We placed no restrictions on the year of publication. Comprehensive search strategies for all searches and databases are included in the supplemental material (Appendix 1).

Selection and data collection processes

In the initial phase of the selection process, two researchers independently reviewed the titles and abstracts after removing duplicate studies. In the second phase, the two researchers further reviewed the full texts of the selected literature. In case of any disagreement, a third party was involved as an arbitrator, and a decision was reached by majority vote. Two independent reviewers collected the data separately and compared the extracted data for consistency. After the initial conversation, any discrepancies were resolved by a third person.

Data items

Two researchers independently extracted and verified the data using a predefined extraction form. We extracted data on the characteristics of the included studies, comprising the following: (a) country, first author, and year of publication; (b) participant numbers, average age, and gender; (c) intervention information data (duration, follow-up period, and control content); and (e) outcome measures. Additionally, the ICT interventions, including telehealth, computer feedback, smart home systems, exergames, and cognitive games were examined. Subsequently, the outcomes of the ICT interventions, including fall rates, the proportion of fallers, balance, fall efficacy, physical function, quality of life, and cognitive function, were evaluated.

Risk of bias

The researchers separately assessed potentially eligible studies for inclusion based on the complete text and addressed any disagreements through discussion. The quality of the included studies was evaluated using the Cochrane Risk of Bias tool (RoB)²² designed for randomized controlled trials (RCTs). Additionally, the RoB for non-randomized studies was assessed using the Risk of Bias Assessment Tool for Non-randomized Studies (ROBANS).²³

Effect measures

Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated for dichotomous data. For continuous data, we conducted a meta-analysis using the mean differences (MDs) with standard deviations (SDs) and 95% CIs. If more than one tool was used, the standard MDs were analyzed. The heterogeneity among the selected studies was evaluated using a chi-squared test with a significance level of p < .100 and I² statistics, including meta-analyses. Data were analyzed using a fixed-effects model when I² was less than 50%.

Synthesis methods

The combination of outcomes from separate studies was obtained based on the assessment of heterogeneity. When studies were determined to have sufficiently homogeneous study designs, a meta-analysis was conducted to determine statistical heterogeneity. Statistical analyses were performed using ORs and fixed-effect models, which were preferred. If significant heterogeneity was observed, we employed the random-effects model for our analysis. Additionally, because the physical functions of the two tools, Timed Up and Go (TUG)²⁴ and Short Physical Performance Battery (SPPB)²⁵ were opposed, the resultant value from one tool was multiplied using a negative (−) before being evaluated. The Cochrane’s Review Manager program (RevMan version 5.4.1) was used for statistical analysis.

Results

Study selection

We initially retrieved 6361 articles from four databases, including Ovid-MEDLINE, Ovid-EMBASE, Cochran Library and CINAHL. After excluding overlapping articles and reviewing the titles and abstracts, 209 of which were selected. After checking the full text, 31 articles were selected. In addition to the database searching, three articles were added by a citation searching. Ultimately, a total of 34 articles^9,26–58 were selected. Among these, 33 studies^{9,26–36,38–58} were RCTs while one³⁷ was a quasi-experiment (Figure 1).

Figure 1.

PRISMA flow diagram of the study selection process.

Study characteristics

The characteristics of the 34 studies^9,26–58 are shown in Table 1. The studies were conducted between 2004 and 2022, and they were performed in North America (n = 12),^{9,32,34,36,37,39,45,46,49,54,55,57} Europe (n = 14),^{28,30,31,35,38,40–42,44,47,48,52,53,58} Oceania (n = 4),^27,43,51,56 and Asia (n = 4).^26,29,33,50 The included studies were categorized according to the participant characteristics, primary outcomes, and intervention methods. The participants were categorized into six groups based on the type of intervention (Table 2): telehealth (n = 11),^{9,26–29,31,33,38,48,49,56} exergame (n = 11),^{34,36,39–42,44,45,50,51,54} computer feedback (n = 3),^30,32,55 smart home systems (n = 3),^52,53,57 cognitive games (n = 5),^{35,37,43,46,47} and fall detector (n = 1).⁵⁸

Table 1.

Characteristics of the selected studies (n = 34).

Author (year)	Country	Study design	Participant characteristics			Eligibility criteria	Main outcomes	Intervention method
Author (year)	Country	Study design	Sample size (I/C)	Mean age ±SD	Women n (%)	Inclusion	Main outcomes	Intervention group	Control group
Oba et al.²⁶ (2022)	Japan	RCT	EIP: 36 LEP: 34	EIP: 69.3 ± 3.9 LEP: 68.8 ± 3.1	33 (47.1)	Age ≥65 Pre-frail/robust based on the Brief Frailty Index (less than three items on the Brief Frailty Index); Can communicate online and operate mobile devices without problems	CS-30, TUG, SOLEO scores, pre- to post participation change in self-rated health, the daily lives after the study (exercise, physical functioning, quality of daily life, psychological well-being, interests, and community involvement)	Active Centenarian Physical Fitness Program; live training consisted of communication and exercise, with a target of 4.5 metabolic equivalent of task, including strength training, gymnastics, aerobic exercise	live training consisted of communication and exercise, with a target of 2.5 METs, including body stretching/yoga.
Delbaere et al.²⁷ (2021)	Australia	RCT	I: 254 C:249	I: 77.1 ± 5.5 C: 77.7 ± 5.5	336 (67.3)	Age ≥70 living in the community, independent in activities of daily living, able to walk household distances without the use of a walking aid, and willing and able to give informed consent and comply with the study protocol.	rate of falls, proportion of people who fell over the first 12 months	StandingTall programme, with exercise equipment (foam cushion, stepping box,exercise mat) health promotion education programme and usual care	health promotion education programme and usual care
Yerlikaya et al.²⁸ (2021)	Cyprus	RCT	ITHE: 18 NHE: 16 CG: 16	ITHE: 70.22 ± 5.53 NHE: 71.81 ± 6.57 CG: 75.62 ± 8.68	35 (70.0)	Age ≥65 Responded to an announcement in randomly selected villages in the province of Nicosia, North Cypurs	Sway, BBS, TUG, TAI, WHL	(ITHE) interactive home-based interactive telerehabilitation using video access platforms (NHE) nonsupervised home exercise using videos	No intervention
Yi et al.²⁹ (2021)	South Korea	RCT	35/35	IG: 76.11 ± 6.31 CG: 77.31 ± 5.57	58 (82.9)	Age ≥65 Ability to walk without a walking aid; ability to communicate; and ability to provide written informed consent	10-m walk test, five times FTSS, grip strength test, TUG, postural sway test, GDS	Remote home-based fall prevention exercise program using live streaming video via smartphones	No intervention (usual daily activities)
Schoon et al.³⁰ (2020)	Netherlands	RCT	43/43	IG: 79.9 ± 5.5 CG: 80.9 ± 7.0	56 (65.1)	Age ≥70 Fell at least once in the previous year and able to walk (with or without a walking aid)	MOS-20, SPPB, TUG, LAPAQ, MMSE SMAS-30, Katz-15 FES-I, CIRS-G	Self-management fall prevention program of mobility with MFD	No intervention Weekly telephone calls from the Falls telephone
Bernocchi et al.³¹ (2019)	Italy	RCT	141/142 Analyzed 122/123	IG:77.9 ± 6.0 CG:79.3 ± 7.0	168 (59.0)	Age ≥65 medium/high fall risk, fall within last year BBS ≤ 45, MMSE > 18 fall during hospitalization	Falls, BBS, EQ-5D, EQ-VAS, TUG, FES, ADL, BI, Time to first fall	-Home Telehealth: Fall prevention by the PT involving individual home exercises, based on the Otago Exercise Program- Tele surveillance	Usual care
Bao et al.³² (2018)	USA	RCT	6/6	IG:76.2 ± 5.5 CG:75.0 ± 4.7	8 (66.7)	Age 65-85, medically stable MOCA >26, has balance concerns, stands unassisted for 10min, walks without an assistive device	ABC, TUG, TUG-COG 10 m walk test TUG-COG, FSST Mini-BESTest 5x Sit-to-stand test, FRT, SOT	Vibrotactile sensory augmentation balance training	Exercises without sensory augmentation
Hong et al.³³ (2018)	South Korea	RCT	15/15 Analyzed 10/13	IG:78.10 ± 5.66 CG:81.54 ±5.07	23 (100.0)	Age ≥65, community-dwelling, FRAS ≥14, no regular exercise 6 months prior, contraindication to exercise or mental illness	BBS, FES-K, Lower body strength, Fear of Falling Questionnaire, Senior Fitness Test, body composition	Telepresence Exercise Program	No intervention
Shake et al.³⁴ (2018)	USA	RCT	47/38	IG:73.59 ± 7.87 CG:73.22± 7.79	90 (85.0)	An adapted version of the telephone MMSE ≤17 Normal or corrected normal vision English as a native language	SPPB EXAMINER cognitive battery Health knowledge test	Bingocize (a series of exercises and multiple-choice questions about health topics related to osteoarthritis and fall risk)	Bingocize (multiple-choice health topic questions only)
Barban et al.³⁵ (2017)	Italy, Greece,Spain, Serbia	RCT	MixT:121 MT: 119 CT: 118 AC: 123	MixT:74.5 ± 7.9 MT:75.5 ± 8.5 CT:74.1 ± 7.2 AC:76 ± 8.8	314 (65.0)	Age ≥65, POMA ≤20, ≥5 years education No major cognitive, behavioral, psychiatric, or systemic disturbance	FES-I, POMA B, POMA G TMT B-A, PF, RAVLT, ROCF GDS, STAI-Y BI, ADL	(MixT): Motor and cognitive training (MT): Motor training only (CT): Cognitive training only	(AC) No intervention
Padala et al.³⁶ (2017)	USA	RCT	15/15	IG:67.5 ± 8.1 CG:69.0 ± 3.8	30 (13.3)	Age≥ 60, community-dwelling, BBS ≤ 52, MMSE ≥ 24 not wheelchair or walker user, no contraindications to exercise, no medical conditions likely to compromise safety	BBS, ABC, SF-36, MMSE Physical Activity Enjoyment Scale	Wii-Fit Program (Yoga, Strength Training, Aerobics, Balance Games)	cognitive exercises using ‘Brain-Fitness’
Blackwood et al.³⁷ (2016)	USA	Quasi-experimental study	19/25	IG:73.79 ± 4.36 CG:75.68 ± 6.88	32 (72.7)	Age ≥65, Good vision, Ambulation Able to communicate in English and independently consent to participate, living in the community Positive history of computer uses (4 or more hours/week)	Five Times Sit to Stand test TUG Gait speeds	Home-based cognitive intervention training (CCT)	Measurement (cognition, fall risk)-only
Broekhuizen et al.³⁸ (2016)	Netherlands	RCT	119/116	IG:64.7 ± 3.0 CG:64.9 ± 2.8	96 (41.0)	Age 60-70 (Respond using GPPAQ and belong to the inactive category) No history of DM or use of glucose-lowering medication Absence of disability impeding increase in physical activity Use of computer with internet connection	QOL (Base/3 month), RAND (the Research ANd Development) 36-item health survey (RAND-36), Moderate-to-vigorous Physical activity	Web-based physical activity program (Direct Life) accelerometer-based activity monitor, a personal website a personal e-coach, wear the activity monitor to measure daily physical activity	Waitlist
Crandall & shake³⁹ (2016)	USA	RCT	7/4	IG:72.29 ± 7.41 CG:73.50 ±6.35	10 (90.9)	Age ≥60, T-MMSE ≥17 Normal/corrected normal vision English as their native language Mobility (not wheel-chair bound)	SPPB, Gait velocity Arm curl test, Body weight Resting blood pressure Health knowledge	Bingocize mobile app (bingo, exercise, and education game)	Bingocize mobile app to play standard bingo
Light et al.⁹ (2016)	USA	RCT	76	76 ± 12	3 (3.9)	2≥ falls in past years, walking 20 feet MMSE ≥24	BBS	Exercise + telehealth	Exercise only
Mirelman et al.⁴⁰ (2016)	Belgium, Israel, Italy, Netherlands, UK	RCT	154/148 Analyzed 146/136	IG:74.2 ± 6.9 CG:73.3 ± 6.4	100 (35.5)	Age 60−90 walk for at least 5 min unassisted Stable medication for the past month 2≥ falls within 6 months MMSE ≥21, no psychiatric comorbidity, history of stroke, brain injury or neurological disorder, lower back/extremity pain, rheumatic/orthopedic disease	incident rate of falls, Gait speed and variability, SPPB, SF-36, UPDRS-III Physical Activity Scale for the Elderly Questionnaire A computerized neuropsychological test battery	Treadmill training plus VR(virtual reality)	Treadmill training
Eggenberger et al.⁴¹ (2015)	Switzerland	RCT	DANCE:24 MEMORY:22, PHYS: 25	DANCE: 77.3 ± 6.3 MEMORY: 78.5 ± 5.1, PHYS: 80.8 ± 4.7	46 (64.8)	Age >70 living independently or in elderly residences walk ≥20 m with/without a walking aid	SPPB, FES-I, GDS Gait performance 6min Walk Test Fall frequency	Virtual reality dance game (DANCE)	Treadmill exercise with cognitive training (MEMORY) Treadmill exercise training only (PHYS)
Gschwind et al.⁴² (2015)	Spain, Australia, Germany	RCT	78(IG) 75(CG)	IG:74.7 ± 6.7 CG:74.7 ± 6.0	93 (61.0)	Age ≥65, Living in the community Able to walk 20m without a walking aid Able to watch TV with or without their glasses from 3m distance Have enough space for system use (3.5 m²)	SPPB, TUG TMT, VST, PEBL, DSC, DSB EQ-5D PPA. WHODAS, PHQ-9, Icon-FES, IPEQ, PAQ-50+	Education booklet Instructed in the use of the iStoppFalls program including balance and muscle strength exercises 2nd home visit, Phone support	Education booklet
Schoene et al.⁴³ (2015)	Australia	RCT	47/43	IG:82 ± 7 CG:81 ± 7	60 (66.7)	Age ≥70, Lived independently Able to walk with or without a walking aid Able to step unassisted on a step pad (step size 25-30 cm) No severe lower extremity pain	SST Processing speed attention/executive function Visuospatial ability concerns about falling depression	Interactive cognitive-motor training (ICMT)	Evidence-based brochure on various health-related topics
Whyatt et al.⁴⁴ (2015)	UK	RCT	38/42	IG:77.18 ± 6.59 CG:76.62 ± 7.28	55(68.8)	Age ≥65 Sheltered accommodation and retirement groups in the local community Normal or corrected vision and hearing No primary medical risk factors for falls, BBS≤54, MMSE≥24	BBS ABC Postural control	Balance game training using ‘Wii Fit’	Record levels of physical activity using a record diary
Schwenk et al.⁴⁵ (2014)	USA	RCT	15/15	IG:84.3 ± 7.3 CG:84.9 ± 6.6	21 (70.0)	Age ≥ 65 Presence of fall risk (TUG ≥ 12 sec) Ability to walk without an assistive device for a minimum of 10 meters Written informed consent	CoM, RCI, TUG, Sway, ankle and hip joint sway measured during eyes open and eyes closed balance test, PP, Alternate step test, Gait speed, Gait variability	Interactive balance training integrating sensor-based visual feedback of movement performance	No intervention
Smith-Ray et al.⁴⁶ (2014)	USA	RCT	23/22	IG:72.47 ± 6.33 CG:71.64 ± 5.39	41 (91.0)	Age ≥65, MMSE≥26 ≥1 fall in past 2 years or unable to stand on one leg for >3s Significant balance/walking impairment Psychotropic medication use Participated in cognitive training recently	BBS 10-meter Gait speed 10-meter gait speed under visuospatial dual-task condition	Computer-based cognitive training program	No intervention
van het Reve et al.⁴⁷ (2014)	Switzerland, Germany	RCT	SBC/SB 84/98 Analyzed 69/76	81.1 ± 8.3 (SBC) 81.9 ± 6.3 (SB)	101 (70.0)	Age ≥65, MMSE≥22 Walk 20m with/without aids Free of rapidly progressive, acute, or unstable chronic illness	SPPB, FES-I, Falls RT hand, foot, TMT reaction times of the upper and the lower channel missed answers of the upper and the lower channel	(SBC group) strength-balance training absolved computerized cognitive training	(SB group) Similar strength-balance training
van het Reve et al.⁴⁸ (2014)	Switzerland	RCT	Social:14 Individual:13 Brochure: 17	75 ± 6	28 (63.6)	Age >65, MMSE≥22, Able to walk 20 meters with or without aids Free of rapidly progressive illness, acute illness, or unstable chronic illness	Program Adherence Gait Analysis (GAITRite)- Single and dual-task walking performance SPPB, FES-I	Tablet-Based Strength-Balance Training Active Lifestyle App (1) Social (tablet guided) (2) Individual (tablet guided)	Brochure guided
Bickmore et al.⁴⁹ (2013)	USA	RCT	132/131	IG:71.7 ± 5.6 CG:70.8 ± 5.2	161 (61.2)	English speaking Inactive (not engaged in regular moderate intensity or greater physical activity ≥3 d/wks. for at least 20 min/d over the previous 6 months) Free of any medication condition limit walking program, stable on all medications for at least 3 months	Average daily step count	tablet computers with touch screens for 2 months Connect their pedometer to the computer using a cable and interact with a computer-animated virtual exercise coach	Control pedometer intervention that only tract step counts
Lee et al.⁵⁰ (2013)	SouthKorea	RCT	27/28	IG:73.78 ± 4.77 CG:74.29±5.20	33 (70.9)	Age ≥65 Diagnosis of type 2 DM Able to communicate	One-leg-standing test, BBS, FRT, TUG, Sit-to-stand test Gait assessment using electronic pressure sensitive walkway (GAITRite®)	VRE (virtual reality exercise) program +DM education (health education)	DM education (health education)
Schoene et al.⁵¹ (2013)	Australia	RCT	15/15	IG:77.5 ± 4.5 CG:78.4 ± 4.5	not reported	Age ≥65, walk 20m without a walking aid Able to step in place unassisted on a step pad MMSE≥24, major cognitive impairment, degenerative disease, problems affecting stepping ability, or unstable health conditions	TMT A, B, TUG, FES, PPA 5x Sit to Stand, INHIB Alternate step test Choice Stepping Reaction Time	Step pad training (DDR game)	No intervention
Tchalla et al.⁵² (2013)	France	RCT	49/47	IG:87.8 ± 6.5 CG:85.3 ± 6.3	74 (77.1)	Age ≥65, living at home registered on the frail elderly people register	Incidence of falls	Home telecare system	No intervention
Tchalla et al.⁵³ (2012)	France	RCT (dynamic random)	94/96	IG:84.9± 6.5 CG:82.0 ± 5.7	142 (77.4)	Age ≥65, living at home Registered on a list of frail elderly people High risk of falls Losing functional autonomy	The incidence rate of falls acceptability of home automation system	Home telecare system Equipped with a light path	No intervention
Szturm et al.⁵⁴ (2011)	Canada	RCT	13/14	IG:80.5 ± 6 CG:81 ± 7	19 (63.3)	Age 65-85, MMSE ≥24 English speaking and understand the nature of the study and provide informed consent Independent in ambulatory functions, with or without an assistive device	BBS, ABC, TUG, CTSIB Spatial-temporal gait parameters	Dynamic balance exercise program coupled with computer games	Typical rehabilitation program
Hagedorn & Holm⁵⁵ (2010)	USA	RCT	15/12	IG:81.5 ± 7.7 CG:81.1 ± 6.0	18 (66.7)	DGI <19, MMSE<20 Able to see visual feedback pictures from a PowerPoint projector screen Able to follow instructions for testing and training	BBS, Dynamic Gait Index, FES-I, TUG, One legend balance MCTSIB, the 30s Sit to Stand, Tandem test. Arm flexion, Muscle force 6 min Walk Test	Computer feedback balance training (CB)	Traditional balance training (TB)
Haines et al.⁵⁶ (2009)	Australia	RCT	19/34	IG:80.9 ± 8.9 CG:80.5 ± 6.5	32 (60.0)	Age >65, discharge to community, gait instability or walks with MA, no severe cardiac disease, cognitive impairment, or lower limb weight-bearing restrictions	Falls, ABC, BOMER, EQ-5D, Walk Test, 5× Sit to Stand, 2 min Frenchay Activities Index	Exercise training with DVD + telehealth	No intervention
Tomita et al.⁵⁷ (2007)	USA	RCT	34/44	IG:72.0 ± 6.0 CG:75.6 ± 3.4	69 (89.5)	Age ≥60, living alone, Difficulty in ADL or IADL due to chronic health conditions, without CI	FIM Motor, MMSE OARS IADL, SIP Movement CHART Mobility	A computer with Internet access and X10-based smart home technology	No intervention
Brownsell et al.⁵⁸ (2004)	UK	RCT	34/21	IG:80 CG:78	not reported	Age ≥75 (Community alarm users) or Age 60–74 (Alarm users experienced a fall in the previous 6 months)	FES EQ-5D	Fall detector from one of three suppliers (Attendo, Tunstall, or Tynetec)	No Fall detector

Note: ABC scale: Activities-specific balance confidence scale; AC: Active control; ADL: Activities of Daily Living; AMT: Abbreviated Mental Test; BBS: Berg Balance Scale; BMI: Body mass index; BOMER: Balance Outcome Measure for Elder Rehabilitation; CES-D: Center for Epidemiologic Studies Depression Scale; CG: control group; CI: Cognitive Impairment; CIRS-G: Cumulative Illness Rating Scale-Geriatric; COM: center of mass; CSQ-8: Client Satisfaction Questionnaire; CSRT: Choice stepping reaction time; CT: Cognitive training; DGI: Dynamic Gait Index; EIP: Exercise-Intensive program; FES: Fall Efficacy Scale; FES-I: Falls Efficacy Scale International; FGA: Functional Gait Assessment; FOFQ: Fear of Falling Questionnaire; FRAS: Fall Risk Assessment Scale; FRT: functional reach test; FT: falls telephone; FTSS: sit to stand test; GDS: Geriatric Depression Scale; GAT: Gait adaptability test; GPPAQ: General Practice Physical Activity Questionnaire; IADL: Instrumental Activities of Daily Living; IG: intervention group; ITHE: Interactive telerehabilitation home exercise; LAPAQ: LASA Physical Activity Questionnaire; LEFS: lower extremity functional scale; LEP: Light-load Exercise program; MA: Mobility Aid; MFD: Mobility Feedback Device; MFES: Modified Falls Efficacy Scale questionnaire; MFS: Morse Fall Scale; MixT: Mixed motor and cognitive; MMSE: Mini–Mental State Examination; MOCA: Montreal Cognitive Assessment adjusted for education level; MOS-20: Medical Outcomes Study 20-item short form; MT: Motor training; N: numbers of participants; NHE: nonsupervised home exercise; NFOGQ: New Freezing of Gait Questionnaire; PHQ: Patient Health Questionnaire; PP: Physical performances; QOL-AD: Quality of Life-AD; RCI: Reciprocal compensatory index (Ankle-hip postural coordination); SF-36: 36-Item Short Form Health Survey; SMAS-30: Self-Management Ability Scale; SPPB: Short Physical Performance Battery; SSES: Self-Efficacy for Exercise Scale; SST: Stroop Stepping Test; Sway: Sway Balance Mobile Application; TAI: Trait Anxiety Inventory; TMT: Trail Making Test; TUG: Timed Up and Go test; VAS: visual analogue scale; WHL: World Health Organization Quality of Life Instrument-Older Adult Module; 3MS: Modified Mini-Mental.

Table 2.

ICT components of home-based fall prevention interventions (n = 34).

Author (year)	Study design	Country	Main Focus	Intervention Group	Control Group	Duration	Dose	Follow-up period
Telehealth (n = 11)
Oba et al.²⁶ (2022)	RCT	Japan	Physical Function	(EIP) Active Centenarian Physical Fitness Program live training consisted of communication and exercise, with a target of 4.5 metabolic equivalent of task, including strength training, gymnastics, aerobic exercise	(LEP) live training consisted of communication and exercise, with a target of 2.5 METs, including body stretching/yoga.	12 weeks	60-min exercise program	Baseline 4 weeks 8 weeks 12 weeks
Delbaere et al.²⁷ (2021)	RCT	Australia	rate of falls the proportion of fallers over 12 months	two hours of StandingTall per week and health education	health education	2 years	2 hours per week	Baseline 6 months 12 months 24 months
Yerlikaya et al.²⁸ (2021)	RCT	Cyprus	Balance Mobility Postural sway QOL Anxiety	(ITHE) home-based balance and strength exercises with a physiotherapist via video access platform (e.g., WhatsApp, Google Meet) (NHE) home-based balance and strength exercises without supervision, using video	No intervention	8 weeks	40 minutes a day, three times a week	Baseline 8 weeks
Yi et al.²⁹ (2021)	RCT	South Korea	Balance Strength Mobility Gait analysis Depression	Remote home-based fall prevention exercise program using live streaming video via smartphones	No intervention (usual daily activities)	8 weeks	40 minutes a day, twice a week	Baseline 8 weeks
Bernocchi et al.³¹ (2019)	RCT	Italy	Proportion of fallers Balance QOL Mobility Functional	fall prevention program based on the Otago Exercise Program	Usual care	6 months	(Exercise) 30 minutes, at least twice a week (phone call) every 2 weeks	Baseline, 6 months
Hong et al.³³ (2018)	RCT	South Korea	Body Composition Physical Function Falls Efficacy Fear of Falling	The telepresence using a 15-inch tablet, video conferencing server system, Internet connectivity. The Web based program using elastic resistance bands and balance exercise	No intervention	12 weeks	20-40 minutes a day, three times a week	Baseline, 12 weeks
Broekhuizen et al.³⁸ (2016)	RCT	Netherlands	Physical activity QOL	Web-based physical activity program (DirectLife) 8-day assessment period Starting 1 week after visit, a target was set to increase the level of activity during 12 week web-based interactive coaching program	Waiting list	12 weeks	Given the option to decrease the personalized goal, within limits or to increase their personalized end goal, dependent on physical activity level of the last week.	Baseline 12 week
Light et al.⁹ (2016)	RCT	USA	Berg Balance Scale	Physical therapy home exercise programs 4times /12 weeks Additional 15min weekly telephone call with standard questions and encouragement	Physical therapy home exercise programs 4times over 12 weeks	12 weeks	4 times over 12 weeks	Baseline, 4 week 8 week, 12 week
van het Reve et al.⁴⁸ (2014)	RCT	Switzerland	Balance Strength Gait analysis Physical Performance Fear of Falling	Interactive balance training integrating sensor-based visual feedback of movement (resistance bands, soft balls): The ActiveLifestyle App social (tablet guided) Individual (tablet guided)	Brochure guided	12 weeks	Twice-weekly 3 balance exercises 5 times a week	Baseline (T0), after 3 months
Bickmore et al.⁴⁹ (2013)	RCT	USA	Daily step count	Computer based physical activity program (Embodied Conversational Agent-ECA) touch screen tablet for 2 months Return tablets (2 month) and use a kiosk computer in the clinic waiting room, Daily conversation, virtual coach	pedometer	2 months 12 months	At least 7 days of first 2 weeks of intervention as a baseline 30 days before 2-month interview 30 days before the 12-month interview	12 month
Haines et al.⁵⁶ (2009)	RCT	Australia	Functional, Balance Proportion of fallers Fall efficacy, QOL Mobility	A DVD ‘Kitchen Table Exercise Program’ and workbook combining lower limb strength and balance exercises. Physiotherapist home visits Telephone follow-up for 1 time/8 weeks	No intervention	8 weeks encouraged to continue	not reported	2 month, 6 month
Exergame (n = 11)
Shake et al.³⁴ (2018)	RCT	USA	Functional performance Fluid cognition Knowledge of two health topics (osteoarthritis, fall risks)	Bingocize app on tablets Bingo games incorporated a series of exercises and multiple-choice questions about health topics related to osteoarthritis and fall risk	Bingocize app on tablets Bingo games incorporated the multiple-choice health topic questions	10 weeks	twice per week	Baseline, within 1 week after week 10
Padala et al.³⁶ (2017)	RCT	USA	BBS, Fear of falling, QOL, Physical activity enjoyment	Wii-Fit Program (Yoga, Strength Training, Aerobics, Balance Games, and Training Plus which includes more complex exercise tasks)	cognitive exercises using ‘Brain-Fitness’ (Memory, Language, Attention, Executive function, visual and spatial domain)	8 weeks	45 minutes, 3 days per week	Baseline, Week 4, Week 8
Crandall & shake³⁹ (2016)	RCT	USA	Functional performance Exercise adherence Knowledge of fall risk and osteoarthritis	Bingocize mobile app on tablets Bingo games combined an exercise and a multiple choice or true/false health education question	Bingocize mobile app on tablets Simple standard bingo	10 weeks	60 minutes, Twice per week	Baseline week 10
Mirelman et al.⁴⁰ (2016)	RCT	Belgium, Israel, Italy, Netherlands, UK	Rate of falls, Gait Cognition Mobility, QOL	Treadmill training plus VR	Treadmill training only	6 weeks	45 min, 3 times per week	1 week Before/after training, 1 month, 6 months
Eggenberger et al.⁴¹ (2015)	RCT	Switzerland	Gait variables Functional fitness Fall frequencies Fear of falling Symptoms of depression	Virtual reality video game dancing (DANCE) 2-3 min/game/song 1-2 min rest periods only if required	Treadmill exercise with cognitive training (MEMORY) Treadmill exercise training only (PHYS)	6 months	1 hour, twice per week	Baseline After 3 months and 6 months 1-year
Gschwind et al.⁴² (2015)	RCT	Spain, Australia, Germany	Physical and cognitive tests Questionnaires for health Fear of falling Number of falls, QOL Psychosocial outcomes	Educational booklet about general health and fall prevention Instruction about iStoppFalls in their home Trained staff installed the iStoppFalls components including a PC, a google TV set top box	Educational booklet about general health and fall prevention	16 weeks	120 min/week for balance exergames, 60 min/week for strength exercises	Baseline, Week 8
Whyatt et al.⁴⁴ (2015)	RCT	UK	Levels of functional balance Balance confidence	A structured balance training program using the tailored balance training games	Record levels of physical activity using a record diary	5 weeks	10 training sessions (2times/wks.) A minimum of 30 minutes per session	Baseline, Week 5
Schwenk et al.⁴⁵ (2014)	RCT	USA	Balance Functional performance User-experience	Balance training including weight shifting and virtual obstacle crossing tasks with visual/auditory real-time joint movement feedback using wearable sensors.	Balance training with an iPad and exercise equipment (foam mat, step box, exercise mat)	4 weeks	Twice a week/ approximately 45 min	Baseline, Week 4
Lee et al.⁵⁰ (2013)	RCT	Korea	Balance, Strength Gait, Falls efficacy	Virtual reality exercise (VRE) program using the PlayStation 2 +DM education (health education)	DM education (health education)	10 weeks	Twice a week/ 50 min	Baseline, Week 10
Schoene et al.⁵¹ (2013)	RCT	Australia	Choice stepping reaction time, PPA, TUG, 5 Sit-to-stand test, AST TMT A, B Fear of falling(icon-FES)	Step pad training (DDR game): The game required participants to step as accurately as possible, both in terms of direction and timing, while synchronizing their stepping with instructions presented on the screen	No intervention	8 weeks	2–3 sessions per week for 15–20 minutes each	Baseline, Week 8
Szturm et al.⁵⁴ (2011)	RCT	Canada	BBS, TUG, ABC, CTSIB Spatiotemporal gait variables	A program using dynamic balance exercise coupled with computer games	typical rehabilitation program (consisted of individual strengthening and balance exercises)	8 weeks	16 scheduled Sessions, 2 sessions per week, Each session lasted 45 minutes	Baseline, After intervention
Computer feedback system (n = 3)
Schoon et al.³⁰ (2020)	RCT	Netherlands	Mental wellbeing Feasibility, Compliance Safety	Self-management fall prevention program and measured their usual gait speed weekly using the MFD	No intervention besides the weekly telephone calls	6 months	twice/week	Baseline, Month 6
Bao et al.³² (2018)	RCT	USA	Balance, Strength Gait performance Fall efficacy, Mobility	Vibrotactile sensory augmentation balance training via the smart phone balance	Exercises without sensory augmentation	8 weeks	Three 45-min exercise sessions per week	Baseline,4 weeks, 8 weeks
Hagedorn & Holm⁵⁵ (2010)	RCT	USA	Strength Physical endurance Balance Falls efficacy	Computerized feedback balance training: progressive resistance muscle strength training Physical fitness training, computer feedback balance training	Traditional balance training: progressive resistance muscle strength training, Physical fitness training	12 weeks	Two weekly sessions of 1.5 hours	Before and after end of training
Smart home system (n = 3)
Tchalla et al.⁵² (2013)	RCT	France	Number of falls	Undertook a fall reduction program and equipped nightlight path for preventing falls at home Requiring a wire senor installed on the floor	A fall reduction program only	12 months	not reported	Baseline, 12 months
Tchalla et al.⁵³ (2012)	RCT	France	Risk of fall	Equipped with light path coupled with tele-assistance service (a remote intercom, an electronic bracelet)	Not equipped	12 months	not reported	Baseline, 12 months
Tomita et al.⁵⁷ (2007)	RCT	USA	FIM, MMSE, IADL Sickness Impact Profile Craig Handicap Assessment and Reporting Technique,	Computer with internet access and X10-based smart home technology	No intervention	24 months	not reported	5times house visit in the first year and 3 times in the second year
Barban et al.³⁵ (2017)	RCT	Italy, Greece, Spain, Serbia	fear of falling balance, gait abilities cognitive, behavioral (mood and anxiety) functional abilities	(MixT) Motor and cognitive training : 30 min of CT and 30 min of MT per session (MT) Motor training only : a set of warm-up procedures by exercises for half of the time of each session to balance and half to gait (CT) Cognitive training only: :Mainly focused on executive functions and attention	(AC) Active control : Entering data into the same platform used the CT	12 weeks	All the treatment: through 24 one-hour sessions twice-a-week	Baseline, End of the treatment after 3 months/ 6 months,
Schoene et al.⁴³ (2015)	RCT	Australia	Stepping, Speed Accuracy in mental rotation Concern about falling	Interact with a computer interface, and videogame technology (four games: Stepper, StepMania, Trail-Stepping, Tetris)	brochure on fall prevention	16 weeks	Three 20-minute sessions per week	Baseline, Week 16, Telephoned at the end of weeks 1, 4, 8 and 12
Smith-Ray et al.⁴⁶ (2014)	RCT	USA	Balance Mobility	Computer-based cognitive training program in a classroom format at the senior/ community centers.	No intervention	10 weeks	60 min per session/ two times per week	Baseline, 10 weeks
Van het Reve et al.⁴⁷ (2014)	RCT	Switzerland, Germany	Physical performance Simple reaction time Executive function Divided attention Fear of falling Falls	SBC group Motor intervention program: -progressive resistance training on age adapted machines -balance training Cognitive intervention program: computer-based and supported the training of cognitive abilities	SB group Motor intervention program: -progressive resistance training on age adapted machines -balance training No cognitive intervention program	12 weeks	Progressive resistance training: twice-weekly 30 minutes Balance training: 10 minutes Cognitive training: 3 times a week for 10 minutes	Fall: retrospectively 6 months, during study 3 month, prospectively 12 month Other: Baseline, 12 weeks
Blackwood et al.³⁷ (2016)	Quasi-experimental study	USA	Fall risk Cognitive Gait speed TUG	Cognitive intervention studies, home based computerized cognitive training (CCT) program: Standardized instructions for playing the games were provided in both verbal and written form	Measurement (cognition, fall risk)-only: With the exception of the posttest reminder phone, control group participants were not contacted during the study period	6 weeks	Three sessions per week / 20-25 minutes/session	Baseline, week 7
Fall detector (n = 1)
Brownsell et al.⁵⁸ (2004)	RCT	UK	Fear of falling Morale and health-related QOL User acceptance Perceived benefits Device performance	Fall detector from one of three suppliers (Attendo, Tunstall or Tynetec)	No Fall detector	17 weeks (SD 3.1)	not reported	Baseline, During 17 weeks (SD 3.1)

Note: FIM: Functional Independence Measures; IADL: Instrumental Activities of Daily Living; ITHE: Interactive telerehabilitation home exercise; MFD: the Mobility Feedback Device; MMSE: Mini-Mental State Examination; NHE: nonsupervised home exercise; PPA: Physiological fall risk; QOL: Quality of life; VR: virtual reality.

Quality assessment

Figure 2 depicts the quality assessment results. The Cochrane RoB tool was used to evaluate the 33 RCTs.^{9,26–36,38–58} The results revealed that seventeen studies^{9,26–30,32–34,38,40–43,49,51,56} had a low RoB for performance bias. Twenty-six studies^{9,26–35,38–43,45,47–49,51–54,56} had a low RoB for detection bias. Twenty-four studies^{26–32,35,36,38,40–43,45,47–50,52–54,56,58} were rated as having a low RoB for attrition bias. Twenty-two studies^{26–31,33,34,36,38,40,42,43,45,47–51,54,56,57} were rated as having a low RoB for reporting bias. Regarding selection bias, random sequence generation was low in 28 selected studies,^{9,26–36,38–43,45,47–51,54,55,57,58} and allocation concealment was low in 19 selected studies.^{26–31,33–36,38–41,43,45,51,54,56} One non-RCT³⁷ was assessed based on the ROBANS tool, the following risks were low: participant selection, confounding variable, measurement, blinding, incomplete outcome data, and selective outcome reporting.

Figure 2.

A summary of the results of quality assessment. (A) Risk of bias summary (B) Risk of bias graph.

Effect of ICT interventions on fall rates

Among the 34 studies, five studies^{27,40,47,53,56} provided the fall rates. A summary of the fall rates is presented in Table 3. However, synthesizing the results was challenging due to variations in units (e.g., person-year, incidence rate ratio, or follow-up durations) across the included studies. Due to the limited availability of data on fall rates in our review, conducting a meta-analysis was not feasible.

Table 3.

A summary of the effectiveness of ICT interventions on fall rates.

Author (year)	Types of ICT Intervention	Fall monitoring period		Fall events		The number of people falling
Author (year)	Types of ICT Intervention	Fall monitoring period		Fall Rates	Rate Ratio [95% CI]	Fall Risk (%)	Risk Ratio [95% CI]
Delbaere et al.²⁷ (2021)	Exercise program via tablet	During	24 months	I: 0.60 ± 1.05 falls per 12 months 0.57 ± 0.95 falls per 24 months	IRR = 0.84 [0.62, 1.13]	I: 34.6%^a	0.90 [0.67, 1.20]^a
Delbaere et al.²⁷ (2021)	Exercise program via tablet	During	24 months	C: 0.76 ± 1.25 falls per 12 months 0.72 ± 1.17 falls per 12 months	IRR = 0.84 [0.62, 1.13]	C:40.2%^a	0.87 [0.68, 1.10]b
Mirelman et al.⁴⁰ (2016)	Exergame (VR)	After	6 months	I: 6.00 falls per 6 months (95% CI 4.36-8.25)	IRR = 0.58 [0.36, 0.96]	I: 62/146 (42%)^c	Not reported
Mirelman et al.⁴⁰ (2016)	Exergame (VR)	After	6 months	C: 8.27 falls per 6 months (95% CI 5.55-12.31)	IRR = 0.58 [0.36, 0.96]	C: 49/136 (36%)^c	Not reported
van het Reve et al.⁴⁷ (2014)	Cognitive games (computer-based)	During	3 months	I: 0.01 ± 0.07 falls per month (fall rate was reduced by 83%)	Not reported	Not reported	Not reported
		During	3 months	C: 0.01 ± 0.05 falls per month (fall rate was reduced by 81%)	Not reported	Not reported	Not reported
		After	6 months	I: 0.05 ± 0.07 falls per month (fall rate was reduced by 46%) C: 0.02 ± 0.04 falls per month (fall rate was reduced by 58%)	Not reported	Not reported	Not reported
Tchalla et al.⁵³ (2012)	Smart home system	During	12 months	I: 30.60 per 100 person-year	Not reported	I: 29/94 (30.9%)	OR = 0.33 [0.17, 0.65]^d
Tchalla et al.⁵³ (2012)	Smart home system	During	12 months	C: 51.90 per 100 person-year	Not reported	C: 48/96 (50.0%)	OR = 0.33 [0.17, 0.65]^d
Haines et al.⁵⁶ (2009)	Video exercise	After	6 months	I: 21 falls per 6 months	IRR = 0.72 [0.33, 1.57]	I: 11/19	OR = 0.96 [0.31, 3.00]
Haines et al.⁵⁶ (2009)	Video exercise	After	6 months	C: 51 falls per 6 months	IRR = 0.72 [0.33, 1.57]	C: 20/34	OR = 0.96 [0.31, 3.00]

Note. I: intervention group; C: control group; IRR: incidence; RR: relative risk; CI: confidence interval; VR: virtual reality; OR: odds ratio

(1) Fall Rates = the total number of fall events per unit of time of fall monitoring. e.g., 100 person-years

(2) Rate Ratio = comparison of the fall rates between the intervention and control group. e.g., incidence rate ratio (IRR)

(3) Fall Risk = the number of people falling at least once

(4) Risk Ratio = comparison of the fall risk between the intervention and control group. e.g., relative risk (RR) or odds ratio (OR)

^a12 months

^b24 months

^cThe number of people who had two or more falls in a given period

^dIndoor falls (analysis by the multiple logistic regression)

Effect of ICT interventions on the proportion of fallers

A meta-analysis was conducted on six studies^{31,40,47,52,53,56} that examined the proportion of fallers, and a total of 1059 patients were included. A subgroup analysis was conducted to investigate the effect of fall prevention intervention using ICT on the proportion of fallers. In terms of reducing fall risks, ICT interventions reduce the risk of falls (Figure 3(A)). Telehealth (RR = 0.62, 95% CI [0.45, 0.84]), smart home systems (RR = 0.58, 95% CI [0.44, 0.77]), exergames (RR = 0.58, 95% CI [0.36, 0.95]) have also been shown to reduce fall risks. Moreover, a subgroup analysis was conducted according to the performance bias for the proportion of fallers; the performance bias was categorized into high-risk (n = 4)^31,47,52,53 and low-risk groups (n = 2),^40,56 and the results revealed no unclear risk. There was no significant subgroup difference in RR (Chi² = 0.35, p = .56). In the group with a high risk of performance bias, the RR was 0.62 (95% CI [0.51, 0.74]), and the RR in the low-risk performance bias group was 0.70 (95% CI [0.47, 1.00]). The RR for the overall proportion of fallers is 0.63 (95% CI [0.54, 0.75]).

Figure 3.

Meta-analysis of the effects of information and communication technology interventions (A) Proportion of fallers, (B) Balance.

Effect of ICT interventions on balance

Eight studies reported balance measured by the Berg Balance Scale (BBS).⁵⁹ The ICT interventions for fall prevention improved BBS scores in the intervention groups (mean difference [MD] = 3.77, 95% CI [2.84, 4,71]). To investigate the effects of ICT interventions on balance by type, a subgroup analysis was performed (Figure 3 (B)) which included telehealth (n = 4),^9,28,31,33 exergame (n = 3),^36,44,54 and computer feedback (n = 1).⁵⁵ Telehealth and exergame improved the balance (MD = 3.30, 95% CI [1.91, 4.68], MD = 4.40, 95% CI [3.09, 5.71], respectively) while computer feedback did not (MD = 0.70, 95% CI [-4.44, 5.84]).

Effect of ICT interventions on physical function

Physical function was assessed using the TUG and SPPB tests. ICT interventions were effective in improving physical function (SMD = 0.37, 95% CI [0.10, 0.64]) (Figure 4(A)). Unlike SPPB scores, higher TUG scores indicated worse physical performance. Thus, a negative value was calculated for the TUG test value to confirm physical function.

Figure 4.

Meta-analysis of the effects of information and communication technology interventions (A) Physical function, (B) Fall efficacy.

Effect of ICT interventions on fall efficacy

Eight studies used the Fall Efficacy Scale (FES)⁶⁰ to determine fall effectiveness. This meta-analysis included 1009 participants (Figure 4(B)). ICT interventions in older adults have shown positive effects on fall prevention (MD = −0.99, 95% CI [−1.33, −0.65]). A higher FES score indicates a stronger fear of falling,⁶⁰ suggesting that reducing the FES score in the intervention group was effective in enhancing their confidence in preventing falls.

Effect of ICT interventions on quality of life

Four studies with 954 participants reported post-intervention quality of life measured by the EuroQol-5 Dimension (EQ-5D) or EuroQol Visual Analog Scale (EQ-VAS).⁶¹ The ICT interventions on quality of life, measured by EQ-5D have been shown to be ineffective (MD = 0.03, 95% CI [-0.03, 0.09]) while ICT intervention measured through EQ-VAS have been shown effective (MD = 5.84, 95% CI [1.32, 10.37]). (Appendixes 2A and B).

Effect of ICT interventions on cognitive function

Three studies^27,42,47 identified the effects of ICT interventions on cognitive function using Trail Making Tests (TMT) A and B.⁶² The TMT evaluates visual search, scanning, processing speed, mental flexibility, and executive skills.⁶³ Cognitive function assessed using either TMT A or TMA B was not shown to be effective (MD = −4.56, 95% CI [−14.49, 5.36], MD = 2.70, 95% CI [−5.62, 11.02], respectively) (Appendix 3).

Discussion

To assess the effectiveness of fall prevention interventions for older adults in the community using ICT, we focused more on fall prevention interventions using the most recent types of ICT (e.g., telehealth, computerized balance training, exergaming, mobile application education, or virtual reality exercises) and excluded studies where interventions were conducted simply by telephone interviews or home visits. This study found that ICT interventions, including telehealth or smart home systems, can be effective in reducing the proportion of fallers and improving balance, while the effect of cognitive games and computer feedback on balance remains unclear. In addition, board-type exergames can be effective in reducing falls among the various types of exergames. Finally, community-based non-face-to-face fall prevention interventions improved physical functions and reduced the fear of falling in older adults.

Effects on fall risk and balance by intervention types

Our review found that telehealth interventions for older adults significantly reduced the number of fallers and improved their balance. Consistent with our study, a recent systematic review in community-dwelling adults with neurological conditions found that telehealth interventions significantly improved balance outcomes.⁶⁴ A systematic review⁸ found that combined intervention including telehealth and balance-related exercises reduced the proportion of fallers. When considering intervention adherence, telehealth might have disadvantages as intervention adherence rates tend to decrease over time.⁶⁴ However, ICT interventions, which enhance intervention adherence, can mitigate the disadvantages of telehealth as a non-face-to-face intervention. Thus, in older adults, incorporating balance exercises into ICT interventions is crucial to prevent falls and achieve positive outcomes in the future.

Smart home devices lowered the number of falls among older persons in our review. The combination of a light path and teleassistance decreased falls at home in the frail senior group. Consistent with our findings, a systematic review⁶⁵ demonstrated that smart homes and health monitoring systems provide clinical advantages by 66.66% over no intervention or alternative forms of intervention. By using automated assistance, smart home systems can enhance the comfort of older adults without disrupting their usual way of life.⁶⁶ Other studies have also demonstrated that smart home systems play a crucial role in promoting the safety and well-being of older individuals, enabling them to spend more time in familiar environments.^67,68 Therefore, the appropriate use of smart home systems, in conjunction with exercise, can be one intervention option for fall prevention in older adults.

Our study found that fall prevention interventions using exergames reduced the number of fallers and improved their balance. Consistent with our findings, other systematic reviews also indicate that exergame-assisted rehabilitation was effective in reducing the incidence of falls,⁶⁹ and improving functional balance in older adults.⁷⁰ Contrastingly, one study⁷¹ demonstrated statistically nonsignificant improvement in the balance following a 3-week exergame intervention. Furthermore, another systematic review⁸ reported that exergames exhibited no significant effect, while telehealth and exercise interventions led to improvements in balance. This inconsistency can be explained that among various types of exergames, board-based exercises were effective in reducing falls in our review. Balance exercises (e.g., balance training, resistance, and aerobic exercises) and board training (e.g., Wii Fit training) have been proven to be effective in increasing the balance ability of older adults.⁷² As the adherence of older adults to conventional physical exercise programs is poor, interventions using exergame technology have been developing rapidly.⁷³ Although the positive effects of exercise are well known, only 30% of older adults exercise regularly.⁷⁴ Therefore, we suggest that exergames can be a good option for ICT fall prevention interventions in older adults. This approach is not only clinically important for achieving positive outcomes, but it is also helpful in promoting exercise adherence. Moreover, since most exergames are targeted towards younger generations, developing older adult-friendly exergames that enhance usability for older adults⁷⁵ may be beneficial for promoting older adults’ health.

The effect of computer feedback on balance was unclear in our meta-analysis. However, the analysis was based on only one study. While the group that received computer feedback balance training showed better balance improvement than that of the group that received traditional balance training, the differences were not statistically significant, highlighting the need for further research. Utilizing virtual reality or machine-based approaches for balance training offers a more diverse and engaging exercise experience compared to that associated with conventional balance training. Further research is needed to validate their effectiveness in improving balance ability.

We found that cognitive games did not significantly reduce the proportion of falls or improve balance. Nevertheless, interactive cognitive-motor interventions can help older adults enhance physical and cognitive functions.⁷⁶ As the causes of falls are multidimensional, including both physical and cognitive factors, cognitive interventions are necessary to prevent falls. Cognitively impaired older adults have a significantly increased risk of falling, at least twice that associated with their cognitively normal counterparts.⁷⁶ Although the exact mechanisms by which people with cognitive impairment are more likely to fall are not fully understood, reduced cognitive function may result in decreased attentional resource allocation while walking.⁷⁷ A meta-analysis⁸ that included a study⁴⁶ that used the Posit Science program, which is a balance-related cognitive game, reported no significant effect on balance. However, the included study⁷⁷ identified a positive effect on small sample size, suggesting low statistical power. Therefore, future studies, such as large-scale RCTs including cognitive game interventions for fall prevention, are warranted.

Effects on physical function

In our meta-analysis, individual studies used different instruments (i.e., TUG and SPPB) to evaluate physical function. Videoconferencing exercise is reportedly effective in improving physical function among patients with chronic diseases.⁷⁸ A review study⁷⁵ also revealed that Wii exergames had a positive impact on physical function improvement. However, another meta-analysis⁷⁰ found that the use of Wii Fit exercise did not show significant improvement in muscle strength. We suggest further large-scale studies to examine the effectiveness of ICT fall prevention interventions on physical function.

Effect on fall efficacy

Our review indicates that ICT interventions for fall prevention among older adults in the community may have a positive impact on fall efficacy, or in reducing concerns about falling. This finding is consistent with the results of the most recent systematic review.⁸ The FES-I evaluates concern on falling.⁷⁹ One of the most significant consequences of the fear of falling is avoiding activities that can severely influence physical ability over time.⁸⁰ Sedentary behavior in older adults results in decreased mobility and balance, which might lead to a tendency to fall or fear of falling.⁸¹ Fear of falling may increase one’s chance of falling, and apprehensive older adults commonly avoid mobility tasks, such as reaching and walking.⁸⁰

Effect on quality of life

In our review, ICT fall prevention interventions showed improved quality of life outcomes when measured with EQ-VAS; however, when measured with EQ-5D, they did not show an improvement in the overall quality of life. Consistent with our study, a systematic review⁶⁶ in older adults with chronic conditions found that telemonitoring did not confer significant improvement on their quality of life. Future studies should consider health-related quality of life needs as an outcome measure because it includes multiple aspects of a healthy life measuring functionality and well-being in physical, psychological, and social domains.^82,83

Limitations

Our study was an updated meta-analysis that assesses the effectiveness of ICT interventions (e.g., telehealth, computerized balance training, exergaming, mobile application education, or virtual reality exercises). Moreover, we performed a subgroup analysis according to the type of intervention to identify the best interventions. However, our study had several limitations. First, the included studies had small sample sizes. Further studies with larger sample sizes are required to enhance the level of evidence. Second, conducting a meta-analysis of falls was challenging as the intervention contents could not be easily standardized. Third, due to the variety of study types and the number of samples, comparative analysis according to intervention type was also difficult. Finally, studies that include the assessment of intervention duration and session time are required to confirm the effectiveness of fall prevention interventions using ICT for older adults. Most studies included the motivational benefits of ICT in encouraging older adults to exercise. Compliance may be increased by using interventions that entertain and motivate. However, such novel ICT interventions often tend to decrease motivation effects with time. Thus, one has to be cautious when generalizing the results of the short-term ICT intervention.

Clinical implications

Fall prevention interventions using ICT is critical for the health and well-being of older adults and helps in decreasing the social and economic burden. In the midst of COVID-19 pandemic, to prevent falls of community-dwelling older adults, ICT interventions are preferred more considering social factors, increasing access to healthcare and reducing costs to prevent falls. Providing a safe environment with a smart home system can be an important factor in preventing falls in older people. Since improving balance function is an important factor in reducing falls in older adults, it is important to increase interest by using telehealth or exergames to encourage older people to improve their balance and exercise independently. The completion of non-face-to-face interventions decreases over time. Therefore, using telehealth is necessary to increase the implementation of non-face-to-face fall prevention measures in older people. The assessment of fall prevention interventions utilizing ICT may facilitate the development and implementation of fall prevention strategies.

Conclusions

ICT fall prevention interventions, including telehealth or smart home systems, have a positive effect in reducing the risk of falls and improving balance. Moreover, exergames may be used to reduce falls among community-dwelling older adults and be a way for older people to exercise and stay active in an interesting way. Additionally, ICT fall prevention interventions have a positive effect on improving fall efficacy. It is meaningful that ICT interventions have potential applicability during pandemics or in situations where face-to-face interventions are difficult to implement. The most optimal ICT interventions for fall prevention should be used. However, they did not improve individuals’ quality of life and cognitive functions. Future investigations on telehealth, smart home systems, or exergames are needed to motivate older adults to exercise, prevent falls, and improve their quality of life.

Supplemental Material

Supplemental Material - Effects of community-based fall prevention interventions for older adults using information and communication technology: A systematic review and meta-analysis

Supplemental Material for Effects of community-based fall prevention interventions for older adults using information and communication technology: A systematic review and meta-analysis by Kayoung Lee, Jungeun Yi, and Seon Heui Lee in Health Informatics Journal.

Footnotes

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research was financed in part by a grant from the Korea Health Technology R & D Project, administered by the Korea Health Industry Development Institute (KHIDI), and funded by the Republic of Korea’s Ministry of Health and Welfare (grant number: HI21C0575).

Ethical statement

ORCID iDs

Kayoung Lee

Jungeun Yi

Seon-Heui Lee

Supplemental Material

Supplemental material for this article is available online.

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