Effectiveness of the non-face-to-face comprehensive elderly care application “smart silver care” for community-dwelling elderly: A randomized controlled trial

Abstract

Background

Quality of life for the elderly has become an important issue, and services aimed at improving it have typically been provided face-to-face. However, coronavirus disease 2019 has limited the use of face-to-face services, and the need to convert such systems to online interfaces has emerged.

Objective

This study evaluates the effectiveness of a non-face-to-face comprehensive elderly care application called “Smart Silver Care.”

Methods

This study was designed as a randomized controlled trial. Sixty community-dwelling elderly individuals were randomly assigned to experimental and control groups in a 1:1 ratio. The participants participated in the “Smart Silver Care” intervention using a tablet and smartwatch based on the programs we provided. The participants performed five tasks, five days a week, consisting of physical, emotional, and cognitive programs. Participants could communicate with the researchers in real-time from their homes, and the researchers could remotely supervise their performance.

Results

We found positive effects on the relevant scales testing fall risk (Activities-Specific Balance Confidence [ABC] Scale, p = 0.028; Timed Up and Go [TUG] test, p = 0.001). However, there was no time × group interaction between the experimental and control groups on the relevant scales for depression and quality of life (Short Form-Geriatric Depression Scale [SGDS]-K: p = 0.225; EuroQol five-dimension five-level [EQ-5D-5L], p = 0.172). While the SGDS-K and EQ-5D-5L did not show statistical significance, we found improvement trends in the experimental group.

Conclusions

The findings of this study show that Smart Silver Care significantly improved the participants’ TUG and ABC scores in community-dwelling elderly, and a qualitative evaluation confirmed that it could be conveniently used by the elderly. Thus, Smart Silver Care offers a feasible intervention to improve the quality of life of the elderly, including physical aspects.

Keywords

community-dwelling elderly non-face-to-face intervention application quality of life elderly care

Introduction

As the global population ages, the elderly are predicted to account for 16% of the total population by 2050.¹ The rapid ageing of the global population has become a major public health issue, and the quality of life (QoL) of the elderly has gained attention.² However, the elderly experience low QoL,³ which is associated with mortality^4,5 and can negatively affect cognitive function.

To improve their QoL, emotional interventions, such as music^6,7 and physical activity^8,9 have been conducted in a face-to-face format. However, due to coronavirus disease 2019 (COVID-19), face-to-face services for the elderly have been restricted,^10,11 making non-face-to-face services essential for the elderly to access these services.¹² Accordingly, non-face-to-face elderly care interventions are being developed, including improving social connectedness through computer videoconferencing¹³ and telephone calls to improve depressive symptoms,¹⁴ and applications for dementia patients.¹⁵ Non-face-to-face elderly care interventions have been shown to positively affect lives of the elderly.¹⁶ Considering these aspects, non-face-to-face interventions can improve accessibility and alleviate disconnection.^17,18

However, despite the demand for comprehensive interventions,¹⁹ existing non-face-to-face elderly care interventions are insufficient in satisfying the physical, emotional, and social needs of the elderly²⁰ and have a high dropout rate.²¹ Moreover, motivation presents a challenge in non-face-to-face elderly care interventions.²² and the difficulty in using digital devices also presents a limitation.²³

To overcome the limitations of the existing studies and interventions, we developed a comprehensive suite of services through an application called “Smart Silver Care,” which encompasses physical activity, emotional support, social communication, music, and cognitive function improvement programs. Several motivational strategies have been used to minimize the dropout rate. Moreover, to increase usability, we designed a simple and intuitive user interface with large buttons and letters, a simple screen, and interventions that can be accessed with minimal clicks. This study aims to evaluate the effectiveness of the Smart Silver Care non-face-to-face comprehensive elderly care application (Figure 1).

Figure 1.

(a) Home screen (b) Online community (c) Listening to music (d) Cognitive game (e) Stamp on completion of the mission.

Methods

Study design and setting

This single-blind, randomized controlled trial (RCT) was conducted between July 2022 and October 2022 in Incheon Metropolitan City, Republic of Korea. Participants were enrolled between April and July 2022. This study was approved by the Gachon University Institutional Review Board (1044396-202203-HR-069-01) and was conducted in accordance with the Declaration of Helsinki.

Sample size

To determine the sample size, G power 3.1.9.7 software (Heinrich Heine University, Dusseldorf, Germany) was used. To calculate the sample size, the probability of alpha error and power were set at 0.05 and 0.95, respectively. Moreover, a moderate effect size of 0.25 was set based on Cohen's methods.²⁴ Therefore, a total sample size of 44 participants was required, and an additional 25% were recruited to compensate for unanticipated attrition.

An initial eligibility screening was conducted by telephone. Those who were satisfied with the phone screening (N = 97) participated in the face-to-face screening. The inclusion criteria were as follows: (1) senior citizens aged 60 years and over living In Incheon Metropolitan City, and (2) individuals who could walk without assistance (e.g., wheelchair). The exclusion criteria were as follows: (1) inability to follow a physical activity program (e.g., Parkinson's disease), and (2) cognitive impairment. The final study population comprised 60 participants.

Randomization and blinding

This study was conducted using simple randomization by a study coordinator who was not involved in the assessment or data analysis. For concealed allocation and blinding, a randomization scheme was created prior to the start of the study using the publicly accessible online software—Research Randomizer (www.randomizer.org)—to assign participants the study numbers in either the experimental or control group in a 1:1 ratio.

Intervention

Intervention delivered to the experimental group

The non-face-to-face comprehensive elderly care application, Smart Silver Care was administered over an 8-week period (July 4, 2022, through August 26th, 2022). Prior to the program's initiation, training was conducted to enhance software usability, and five tasks were assigned daily from Monday to Friday (Tables 1 and 2). The interventions were divided into three categories: physical health, emotional support, and cognitive function improvement.

Physical health

The physical health category included a fall prevention class and walking 7000 steps. A real-time video class exercise program, including gymnastics- and music-based exercises, was conducted through meetings with the researchers using a tablet at home. Walking 7000 steps per day was used to measure the amount of daily exercise after wearing the provided smartwatch, and the exercise records were automatically linked to the tablet. Both interventions were conducted five times per week.

Emotional support

Emotional support consisted of participation in an online community, video calls, and listening to music. The online community provided a space to share with other participants the process of planting tomato seeds and bearing fruits or daily life by posting text and photos five times a week. Video calls twice a week allowed communication with family members by clicking on a face-shaped icon. Music was listened to five times per week by pressing the button for each category of hymns, trots, and popular songs.

Cognitive function

The cognitive function improvement category consisted of games to help improve attention, memory, spatiotemporal ability, language, daily computational activity, and reminiscence ability, which the participants performed three times a week.

Table 1.

Smart silver care weekly schedule.

Intervention	Mon	Tue	Wed	Thu	Fri
Physical health	Fall prevention class	Fall prevention class	Fall prevention class	Fall prevention class	Fall prevention class
Physical health	Walking 7000 steps	Walking 7000 steps	Walking 7000 steps	Walking 7000 steps	Walking 7000 steps
Emotional support	Online community	Online community	Online community	Online community	Online community
		Video call		Video call
	Listening to music	Listening to music	Listening to music	Listening to music	Listening to music
Cognitive function	Cognitive game		Cognitive game		Cognitive game

Table 2.

Smart silver care performance description.

Intervention	Times/week	Minutes/session	Description
Fall prevention class	5	30	Real-time exercise programs such as gymnastics and music-based exercise using a tablet at home.
Walking 7000 steps	5	40 (Or 7000 steps)	As a task to promote exercise, daily activity records are automatically recorded using a smart band and Bluetooth.
Online community	5	-	Plant distributed tomato seeds so subjects can communicate with each other and the researcher by uploading photos or sharing daily life activities.
Video call	2	1	Subjects can make a video call with their family or acquaintances to communicate with them.
Listening to music	5	5	Based on the results of the preference survey of the elderly, hymns, trot, and popular songs are included
Cognitive game	3	30	Composed of six parts: attention, memory, spatial-temporal, language, computational daily activity, and reminiscence ability.

Intervention delivered to the control group

For the initial 8-week period, all conditions except for the intervention were the same, and the control group received the same conditions as the experimental group. This included health education, tablet-usage training, internet access, and the provision of digital devices (tablets and smartwatches). After all interventions and outcome measurements were completed for the experimental group, the participants on the waiting list in the control group also received the intervention. By employing this approach, we minimized potential differences between the experimental and control groups that could impact the results.

Outcomes

Primary outcomes

Primary outcomes were measured using the EuroQol five-dimension five-level (EQ-5D-5L) and the EuroQol visual analog scale (EQ-VAS).

EQ-5D-5L

The is a validated questionnaire for evaluating QoL based on the following five factors: mobility, self-care, usual activities, pain or discomfort, anxiety, or depression. Each dimension is measured with severity labels for five levels (ranging from “no problems” to “extreme problems”).²⁵ The EQ-5D-5L's Cronbach's α was 0.88 in this study.

EQ-VASThe is a subjective generic health evaluation inventory with a range from 0 (worst imaginable health state) to 100 (best imaginable health state).²⁶ The EQ-VAS's Cronbach's α was 0.70 in this study.

Secondary outcomes

Secondary outcomes were measured using the Activities-Specific Balance Confidence (ABC) Scale, the Timed Up and Go (TUG) test, the Korean version of the Short Form-Geriatric Depression Scale (SGDS-K), the Korean version of the Fall Efficacy Scale-International (KFES-I), and the Korean version of the Mini-Mental State Examination, 2nd edition (K-MMSE-2).

ABC

The ABC measures balance confidence in various activities and comprises 16 items, with scores for each item ranging from 0 (no confidence) to 100 (complete confidence).²⁷ The ABC's Cronbach's α was 0.90 in this study.

TUG

The TUG is a tool for screening fall-prone elderly,²⁸ measuring completion time (seconds) from getting up from a chair, walking three meters, and then returning to sit down.²⁹ The longer the length, the higher the fall risk.³⁰ The TUG's Cronbach's α was 0.95 in this study.

SGDS-K

The SGDS-K is a valid and reliable tool for measuring depression and consists of 15 items with a total score ranging from 0 to 15.³¹ The SGDS-K's Cronbach's α was 0.91 in this study.

KFES-I

The KFES-I^32,33 consists of 16 items, with each item measured on a scale from 1 (not concerned at all) to 4 (very concerned).³⁴ The KFES-I's Cronbach's α was 0.92 in this study.

K-MMSE-2

The K-MMSE-2 is the Korean, standardized version of the MMSE³⁵ and its reliability and validity have been confirmed.³⁶ Its total score ranges from 0 to 30. The K-MMSE-2's Cronbach's α was 0.78 in this study.

Satisfaction

We evaluated the Smart Silver Care application's usability using the Mobile App Rating Scale (MARS), a simple tool for classifying and evaluating the quality of mobile health apps.³⁷ The user version of MARS (uMARS) comprises the following items: Engagement (five questions), functionality (four questions), aesthetics (three questions), information (four questions), and subjective items (four questions).³⁸ Each subscale was evaluated on a 5-point Likert scale.³⁹ The MARS's Cronbach's α was 0.94 in this study.

Data collection

All participants provided written informed consent, which included a description of the benefits, a guarantee of identity protection and data confidentiality, an explanation of the voluntary nature of participation, and access to their electronic health records. Both the experimental and control groups completed the primary and secondary outcome measurements at baseline. Intermediate and final outcome measurements were performed at weeks four and eight, respectively. Assessments were performed at weeks 0 (baseline), 4, and 8 at the Namdong-gu Public Health Center in Incheon.

Statistical analysis

This study was performed following the intention-to-treat analysis with IBM SPSS software (version 26; SPSS Inc., Chicago, IL, USA). Using the last observation carried forward method, the last available measurements for each individual before withdrawal from the study were used for analysis. Data were analyzed as frequencies and percentages for categorical variables and means and standard deviations (SDs) for continuous variables. The Kolmogorov–Smirnov test was used to test the normality of the distributions. Pearson's chi-square test and independent t-tests were performed to evaluate differences between groups To examine changes in the outcomes from baseline to weeks 4 and 8, mixed-effects linear regression models for longitudinal, repeated-measures data were used. These models were constructed using the interaction terms between the groups at two levels (experimental vs. control) and time at three levels (baseline, week 4, and week 8). The level of statistical significance was set at α < 0.05.

Results

Sociodemographic characteristics at baseline

Six of the 60 participants were excluded from the trial after randomization (one in the experimental group and five in the control group) due to missing data at weeks 4 and 8. One participant had difficulty using digital devices, two were unwilling to participate, two refused to be assessed due to personal schedules, and one could not continue the study due to health problems. Thus, a total of 54 participants (29 in the experimental group and 25 in the control group) completed the study (Figure 2).

Figure 2.

Study flowchart.

All baseline functions and general characteristics of the experimental and control groups were balanced. The average age of the experimental group was 71.4 years (SD = 5.1), and that of the control group was 70.8 years (SD = 6.6). In addition, both the experimental and control groups had the same sex ratio of 7 men (23.3%) and 23 women (76.7%). The mean EQ-5D-5L score of the experimental group was 0.74 (SD = 0.18), and that of the control group was 0.78 (SD = 0.18). The mean EQ-VAS score of the experimental group was 67.7 (SD = 15.0), and that of the control group was 73.8 (SD = 15.5). At baseline, there were no differences between the experimental and control groups in EQ-5D-5L and EQ-VAS scores. All participants were able to complete the baseline assessments (Table 3).

Table 3.

Baseline characteristics of the study participants.

Parameter			Experimental group (N = 30)	Control group (N = 30)	P-value
Parameter			N(%)	N(%)	P-value
Age, mean ± SD, y			71.4 ± 5.1	70.8 ± 6.6	0.728
Sex	Male		7(23.3)	7(23.3)	1.000
Sex	Female		23(76.7)	23(76.7)	1.000
Education	Illiterate		2(6.7)	3(10.0)	0.396
	Elementary school		9(30.0)	5(16.7)
	Middle school		8(26.7)	7(23.3)
	High school		10(33.3)	10(33.3)
	≥ College		1(3.3)	5(16.7)
Living arrangements	Alone		19(63.3)	13(43.3)	0.121
Living arrangements	With family		11(36.7)	17(56.7)	0.121
BMI, mean ± SD, Kg/m²			25 ± 2.7	26.4 ± 4.6	0.165
Smoke	Yes		2(6.7)	4(13.3)	0.385
Smoke	No		28(93.3)	26(86.7)	0.385
Drink	Yes		4(13.3)	5(16.7)	0.717
Drink	No		26(86.7)	25(83.3)	0.717
No. of chronic disease, mean ± SD			2.0 ± 1.1	1.9 ± 1.3	0.595
No. of drugs		≤ 3	21(70.0)	23(76.7)	0.559
No. of drugs		≥ 4	9(30.0)	7(23.3)	0.559
EQ-5D-5L, mean ± SD			0.74 ± 0.18	0.78 ± 0.18	0.477
EQ-VAS, mean ± SD			67.7 ± 15.0	73.8 ± 15.5	0.123
ABC, mean ± SD			70.3 ± 24.0	78.3 ± 20.5	0.173
TUG, mean ± SD			10.8 ± 4.1	10.2 ± 2.2	0.484
KFES, mean ± SD			24.7 ± 9.2	22.2 ± 6.7	0.234
SGDS, mean ± SD			4.6 ± 4.3	4.4 ± 4.2	0.880
MMSE, mean ± SD			27.2 ± 2.7	27.6 ± 2.6	0.526

ABC: Activities-Specific Balance Confidence Scale; SGDS: Short Form-Geriatric Depression Scale; EQ-5D-5L: EuroQol five-dimension five-level; EQ-VAS: EuroQol visual analog scale; KFES: Korean version of the Fall Efficacy Scale; MMSE: Mini-Mental State Examination.

Primary outcomes

There was no time × group interactions between the two groups for any primary outcome (Table 4). EQ-5D-5L scores did not differ between the two groups at week 4 (change from baseline mean = 0.03; 95% confidence interval (CI): −0.12 to 0.06) or week 8 (change from baseline mean = 0.05; 95% CI: −0.04 to 0.14) in the experimental group. However, in contrast to the control group, there was a significant change in the EQ-VAS score of the experimental group at week 8 (change from baseline mean = 7.0; 95% CI:1.4 to 12.6); however, this effect was not confirmed at week 4 (change from baseline mean = -0.9; 95% CI: −9.1 to 7.3).

Table 4.

Effects of the smart silver care on primary outcomes.

	Mean ± SD		Change from baseline, Mean(95%CI)		Time × Group interaction
	Week 4	Week 8	Week 4	Week 8
EQ-5D-5L(0-1)					0.172
Experimental group	0.71 ± 0.29	0.80 ± 0.21	0.03(−0.12 to 0.06)	0.05(−0.04 to 0.14)
Control group	0.82 ± 0.17	0.84 ± 0.16	0.03(−0.03 to 0.08)	0.04(−0.02 to 0.11)
EQ-VAS(0–100)					0.173
Experimental group	66.7 ± 21.2	74.7 ± 11.1	−0.9(−9.1 to 7.3)	7.0(1.4 to 12.6)
Control group	73.8 ± 15.8	73.8 ± 20.0	0.0(−9.2 to 9.2)	0.0(−8.8 to 8.8)

EQ-5D-5L: EuroQol five-dimension five-level; EQ-VAS: EuroQol visual analog scale

Secondary outcomes

Regarding secondary outcomes, there was a time × group interaction between the two groups on the ABC (p = 0.028). While no significant difference appeared for the mean change at week 4 (change from baseline mean = 1.4; 95% CI: −7.5 to 10.3), the results show a significant difference at week 8 (change from baseline mean = 10.8; 95% CI:1.3 to 20.2). Since the minimal clinically important difference (MCID) levels in community-dwelling elderly individuals have yet to be determined, they cannot be compared with the results of this study. Nonetheless, a significant change was observed between the baseline and week 8 results in the experimental group.

In addition, the experimental group showed significant improvement on the TUG test (p = 0.001) compared to the control group. There was no significant difference for the mean change at week 4 (change from baseline mean = 0.2; 95% CI: −0.2 to 0.6); however, the results show a significant difference at week 8 (change from baseline mean = -1.0; 95% CI: −1.4 to −0.5). Although MCID levels in the TUG have been variously reported in certain diseases, there have been no specific studies on improving function in the community-dwelling elderly; thus, we referred to the most relevant MCID (0.8 s) from hip osteoarthritis studies.⁴⁰ The changes between baseline and week 8 (change from baseline mean = -1.0; 95% CI: −1.4 to −0.5) were considered the MCID for the experimental group.

There was no time × group interaction between the two groups in terms of the KFES (p = 0.287), SGDS (p = 0.225), and MMSE (p = 0.101); however, the SGDS score significantly improved in the experimental group at week 8 compared to the control group (change from baseline mean = −1.4; 95% CI: −2.5 to −0.3). Similarly, the MMSE score significantly improved in the experimental group at week 8 compared to the control group (change from baseline mean = 1.3; 95% CI: 0.2, 2.5) (Table 5).

Table 5.

Effects of the smart silver care on secondary outcomes.

	Mean ± SD		Change from baseline, Mean(95%CI)		Time x Group interaction
	Week 4	Week 8	Week 4	Week 8
ABC(0–100)					0.028
Experimental group	71.7 ± 23.2	81.1 ± 21.8	1.4(-7.5 to 10.3)	10.8(1.3 to 20.2)
Control group	83.1 ± 21.0	81.7 ± 23.1	4.8(1.1 to 8.5)	3.4(-3.4 to 10.3)
TUG(s)					0.001
Experimental group	11.0 ± 4.2	9.9 ± 4.1	0.2(-0.2 to 0.6)	-1.0(-1.4 to -0.5)
Control group	9.8 ± 2.4	10.2 ± 2.6	-0.4(-1.1 to 0.2)	-0.2(-0.7 to 0.7)
KFES(0–100)					0.287
Experimental group	23.8 ± 8.0	22.5 ± 8.6	-0.9(-3.2 to 1.4)	-2.2(-5.4 to 0.9)
Control group	23.2 ± 8.4	21.6 ± 7.2	1.0(-1.0 to 3.0)	-0.6(-2.5 to 1.4)
SGDS(0–15)					0.225
Experimental group	4.2 ± 4.1	3.2 ± 3.2	-0.4(-1.8 to 1.1)	-1.4(-2.5 to -0.3)
Control group	3.2 ± 4.5	3.4 ± 4.4	-1.2(-2.8 to 0.4)	-1.0(-2.1 to 0.1)
MMSE(0–30)					0.101
Experimental group	28.5 ± 1.8	28.5 ± 1.4	1.3(0.4 to 2.3)	1.3(0.2 to 2.5)
Control group	28.0 ± 2.8	27.3 ± 5.5	0.3(-0.6 to 1.2)	0.5(-0.5 to 1.6)

ABC: Activities-Specific Balance Confidence Scale; SGDS: Short Form-Geriatric Depression Scale; MMSE: Mini-Mental State Examination; KFES: Korean version of the Fall Efficacy Scale; TUG: Timed Up and Go.

Satisfaction

The total uMARS score average was 4.1 (SD = 0.7), and the second highest was aesthetics (4.3; SD = 0.8), followed by engagement (4.2; SD = 0.7), information (4.2; SD = 0.7), functionality (4.0; SD = 1.0), and finally overall quality (3.9; SD = 0.7) (Table 6).

Table 6.

Evaluation of smart silver care using user version of Mobile App Rating Scale (uMARS) (n = 29).

Engagement	Functionality	Aesthetics	Information	Overall quality	Total uMARS
4.2 ± 0.7	4.0 ± 1.0	4.3 ± 0.8	4.2 ± 0.7	3.9 ± 0.7	4.1 ± 0.7

Discussion

To the best of our knowledge, this is the first RCT to measure the effectiveness of a non-face-to-face comprehensive elderly care intervention that includes physical, emotional, and cognitive dimensions in community-dwelling elderly. The Smart Silver Care application, which provides non-face-to-face integrated care for the elderly, was administered to community-dwelling elderly participants 5 days a week for 8 weeks. There were no adverse events related to the intervention, suggesting that Smart Silver Care is safe for the elderly.

Our findings suggest that 8 weeks of Smart Silver Care as a digital service that can be provided to the elderly can potentially have a positive impact on their lives. First, the participants were motivated using real-time individual feedback and incentive strategies, resulting in a very low dropout rate. Second, the results indicate that Smart Silver Care is effective in improving participants’ measurements on fall assessment tools such as the TUG test and ABC. Third, although its effects on QoL and depression were not statistically significant, the results showed an improvement trend in the experimental group. Finally, in the qualitative usability evaluation of Smart Silver Care, a high score of 4.1 out of 5.0 was achieved compared to other applications for the elderly, with a score of 3.2 5.0.⁴¹

Overall, we found that Smart Silver Care was well-suited to the elderly, with a low overall dropout rate of only 10%. Previous studies have reported overall dropout rates of 28%,⁴² 16%,⁴³ demonstrating the limitations of non-face-to-face interventions for the elderly. With respect to the dropout rates in the experimental group, our study reported rates of 3%, whereas previous studies reported rates of 17%^42,43 and 48%.²¹ To reduce the dropout rate and encourage active engagement, five strategies were employed. First, we aligned with previous research¹⁸ that emphasized the importance of increasing knowledge about the use of digital devices for online services targeting physical activity promotion among the elderly. We highlighted the significance of educational scaffolding and also conducted pre-education, which likely had a positive impact on the low dropout rate. Second, we enhanced usability by simplifying the interface's configuration. Usability problems, such as the difficulty of screen movement, act as obstacles to the elderly's use of technology.⁴⁴ Considering the characteristics of the elderly, we designed an interface with large icons and eye-catching colors. Moreover, to minimize the difficulties in using digital devices, participants received helpdesk support throughout the participation period. During this period, if any issues arose with the device's usage, participants could contact the helpdesk by phone or request in-person assistance. Third, real-time feedback was provided to enhance engagement. Participants received interactive feedback and communication to develop rapport. Fourth, incentives were offered to encourage participation and provide continuous motivation for active engagement. Thus, participants received rewards on a 4-week cycle. Finally, tablet pop-up alarms and mobile messages played a crucial role in encouraging participation. The participants received participation reminders through tablet pop-ups, allowing them to access the mission screen with a single touch. Additionally, the participants were encouraged to engage in the intervention and communication was facilitated through mobile messages.

Regarding physical health, the intervention was effective in improving the participants’ TUG and ABC scores. These are physical- and psychological-based fall measurement tools, respectively,⁴⁵ suggesting improved muscle strength and balance. Significant improvement in TUG results is consistent with previous studies,⁴⁶ demonstrating that elderly-centered, non-face-to-face programs can improve physical ability. Unlike a previous study⁴⁷ that conducted physical interventions using a mobile platform, our study reported significant improvements between the experimental and control groups. We believe that these contrasting results can be attributed to the interactive nature of our real-time classes, which allowed for direct interaction with the participants, ensuring that their needs were met. Second, strategies that allow the elderly to participate in an exercise class in a pleasant and exciting manner are another factor contributing to the improvement of physical ability. The use of music and dancing in the exercise and rhythmic programs may have enhanced the effectiveness of the physical activity intervention. These strategies allow the elderly to participate enthusiastically in fall prevention classes, leading to improved physical ability. Moreover, walking 7000 steps may have a positive effect on exercise sustainability. Many participants reported that they walked in the park daily and successfully completed 7000 steps. They also showed positive changes in their daily indoor and outdoor activities and expressed confidence that they could more easily climb stairs or ramps. Taken together, direct interactions, pleasure, and appropriate missions can contribute to improving the physical ability of the elderly.

Regarding depression and QoL, there were no statistically significant changes in the experimental group; however, there was a trend toward improvement. Regarding the SGDS-K, as in a previous study,⁴⁸ we could not find a significant difference in the time × group interaction. This may be due to ceiling effects; the participants were independent elderly individuals who were capable of conducting the interventions, and their baseline SGDS-K score was low, indicating a high function for depression. Therefore, a statistically significant improvement in the experimental group could not be confirmed within the short period of 8 weeks. However, at the end of the study period, the mean SGDS-K score in the experimental group decreased from the baseline. Regarding the EQ-5D-5L, we could not confirm any improvement in QoL, but it was possible to confirm an improvement trend. In a different study,⁴⁷ QoL improvement was demonstrated; however, the research period was longer at 12 months, and the sample size and measurement tools also differed. Therefore, extending the research period, increasing the sample size, and using various QoL measurement tools could help confirm improvements in QoL.

Finally, the high uMARS of Smart Silver Care suggests its usability for the elderly, achieving a score of 4.1 and surpassing the average score of 3.2 for other applications designed for the elderly.⁴¹ The high score for aesthetics (4.3 out of 5) and the engagement and information categories (4.2 out of 5) suggests that the elderly participants used Smart Silver Care with interest and found it both aesthetically pleasing and helpful. Our elderly-centered design strategy, which included large letters and eye-catching menu colors, may have contributed to these high scores. Overall, the evaluation of Smart Silver Care indicates its usability for the elderly and its potential to improve their lives.

Limitations

Despite its strengths, this study has several limitations. First, the brief follow-up period and insufficient sample size may have been insufficient to record changes in the EQ-5D-5L and SGDS-K scores. Both indicators showed trends of improvement, and therefore, future research should consider expanding the study duration and sample size to examine long-term outcomes. Second, despite pre-study training and helpdesk availability during the study period, it was difficult for the elderly to adapt to the new devices. Given their characteristics, further studies should provide sufficient education to allow elderly participants to adapt to interventions more easily. Third, this study could not account for differences in the acceptability of a new digital device by each participant. Because differences in the acceptance of digital devices can affect compliance, distinguishing between active elderly participants who can accommodate new devices and those who cannot is a useful future expansion. Finally, depending on the subject, various forms of face-to-face and non-face-to-face interventions can be provided to enhance their effectiveness. Despite these limitations, our study highlights the value of interactive interventions and real-time monitoring in reducing dropout rates, improving the physical abilities of the elderly, and monitoring safety incidents. These findings contribute to the growing body of research in this field and emphasize the potential of our approach to elderly care.

Conclusions

This study demonstrates Smart Silver Care's potential as a practical intervention to improve the TUG and ABC scores of community-dwelling elderly. Though a qualitative evaluation showed that Smart Silver Care was a convenient option for community-dwelling elderly, larger sample sizes and longer follow-up periods are required to establish clinically significant outcomes that can improve the QoL of the elderly. The importance of such studies in improving QoL for the elderly is expected to increase.

Footnotes

Contributorship

D.H.H. performed the statistical analyses, contributed to the interpretation of the results, and drafted and completed the manuscript. S.H.L. proposed the research concept, supervised and directed the study, and designed the study. All authors have read and approved the final version of this manuscript.

Declaration of conflicting interests

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

Ethical approval

This study was approved by the Gachon University Institutional Review Board (1044396-202203-HR-069-01).

Funding

This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health and Welfare, Republic of Korea (grant number: HI21C0575).

Guarantor

Seon Heui Lee, Department of Nursing Science, College of Nursing, Gachon University, Incheon, Republic of Korea

ORCID iD

Seon Heui Lee

References

Gaigbe-Togbe

Bassarsky

, et al. World Population Prospects 2022: Summary of Results. New York: United Nations Publication, 2022.

Malicka

Skośkiewicz-Malinowska

Kaczmarek

. The impact of socioeconomic status, general health and oral health on health-related quality of life, oral health-related quality of life and mental health among Polish older adults. BMC Geriatr 2022; 22: 1–15.

Jemal

Hailu

Tesfa

, et al. Geriatric depression and quality of life in North Shoa Zone, Oromia region: a community cross-sectional study. Ann Gen Psychiatry 2021; 20: 36.

Phyo

AZZ

Freak-Poli

Craig

, et al. Quality of life and mortality in the general population: a systematic review and meta-analysis. BMC Public Health 2020; 20: 1596.

Robinson

Kissane

Brooker

, et al. The relationship between poor quality of life and desire to hasten death: a multiple mediation model examining the contributions of depression, demoralization, loss of control, and low self-worth. J Pain Symptom Manage 2017; 53: 243–249.

Talarska

Tobis

Kotkowiak

, et al. Determinants of quality of life and the need for support for the elderly with good physical and mental functioning. Med Sci Monit 2018; 24: 1604.

McCrary

Altenmüller

Kretschmer

, et al. Association of music interventions with health-related quality of life: a systematic review and meta-analysis. JAMA Network Open 2022; 5: e223236–e223236.

Cunningham

O'Sullivan

Caserotti

, et al. Consequences of physical inactivity in older adults: a systematic review of reviews and meta-analyses. Scand J Med Sci Sports 2020; 30: 816–827.

Piercy

Troiano

Ballard

, et al. The physical activity guidelines for Americans. JAMA 2018; 320: 2020–2028.

10.

D'cruz

Banerjee

. ‘An invisible human rights crisis’: the marginalization of older adults during the COVID-19 pandemic–An advocacy review. Psychiatry Res 2020; 292: 113369.

11.

Xie

Charness

Fingerman

, et al. When going digital becomes a necessity: ensuring older adults’ needs for information, services, and social inclusion during COVID-19. J Aging Soc Policy 2020; 32: 460–470.

12.

Sixsmith

Horst

Simeonov

, et al. Older people’s use of digital technology during the COVID-19 pandemic. Bull Sci Technol Soc 2022; 42: 19–24.

13.

Choi

Pepin

Marti

, et al. Improving social connectedness for homebound older adults: randomized controlled trial of tele-delivered behavioral activation versus tele-delivered friendly visits. Am J Geriatr Psychiatry 2020; 28: 698–708.

14.

Choi

Marti

Wilson

, et al. Effect of telehealth treatment by lay counselors vs by clinicians on depressive symptoms among older adults who are homebound: a randomized clinical trial. JAMA Network Open 2020; 3: e2015648–e2015648.

15.

Kerkhof

Pelgrum-Keurhorst

Mangiaracina

, et al. User-participatory development of FindMyApps; a tool to help people with mild dementia find supportive apps for self-management and meaningful activities. Digital Health 2019; 5: 2055207618822942.

16.

Schlomann

Seifert

Zank

, et al. Use of information and communication technology (ICT) devices among the oldest-old: loneliness, anomie, and autonomy. Innov Aging 2020; 4: igz050.

17.

Barbosa Neves

Franz

Judges

, et al. Can digital technology enhance social connectedness among older adults? A feasibility study. J Appl Gerontol 2019; 38: 49–72.

18.

Bosco

McGarrigle

Skelton

, et al. Make movement your mission: Evaluation of an online digital health initiative to increase physical activity in older people during the COVID-19 pandemic. Digital Health 2022; 8: 20552076221084468.

19.

Jovanović

De Angeli

McNeill

, et al. User requirements for inclusive technology for older adults. Int J Human–Comput Interact 2021; 37: 1947–1965.

20.

Khosla

Nguyen

Chu

M-T

. Human robot engagement and acceptability in residential aged care. Int J Human–Comput Interact 2017; 33: 510–522.

21.

Geraedts

Dijkstra

Zhang

, et al. Effectiveness of an individually tailored home-based exercise rogramme for pre-frail older adults, driven by a tablet application and mobility monitoring: a pilot study. Eur Rev Aging Phys Act 2021; 18: –9.

22.

Vassli

Farshchian

. Acceptance of health-related ICT among elderly people living in the community: a systematic review of qualitative evidence. Int J Human–Comput Interact 2018; 34: 99–116.

23.

Rikard

Berkowsky

Cotten

. Discontinued information and communication technology usage among older adults in continuing care retirement communities in the United States. Gerontology 2018; 64: 188–200.

24.

Cohen

. Stafisfical power analysis for the social sciences. New York City, NY: Lawrence Earlbaum Associates, 1988.

25.

Herdman

Gudex

Lloyd

, et al. Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Qual Life Res 2011; 20: 1727–1736.

26.

Rabin

Charro

. EQ-SD: a measure of health status from the EuroQol group. Ann Med 2001; 33: 337–343.

27.

Elboim–Gabyzon

Agmon

Azaiza

. Psychometric properties of the Arabic version of the activities-specific balance confidence (ABC) scale in ambulatory, community-dwelling, elderly people. Clin Interv Aging 2019; 14: 1075–1084. doi:10.2147/CIA.S194777

28.

Shumway-Cook

Brauer

Woollacott

. Predicting the probability for falls in community-dwelling older adults using the timed up & go test. Phys Ther 2000; 80: 896–903.

29.

Podsiadlo

Richardson

. The timed “up & go”: A test of basic functional mobility for frail elderly persons. J Am Geriatr Soc 1991; 39: 142–148.

30.

Greene

O’Donovan

Romero-Ortuno

, et al. Quantitative falls risk assessment using the timed up and go test. IEEE Trans Biomed Eng 2010; 57: 2918–2926.

31.

Bae

Cho

. Development of the Korean version of the geriatric depression scale and its short form among elderly psychiatric patients. J Psychosom Res 2004; 57: 297–305.

32.

Yardley

Beyer

Hauer

, et al. Development and initial validation of the falls efficacy scale-international (FES-I). Age Ageing 2005; 34: 614–619.

33.

Park

Cho

Kwon

, et al. Reliability and validity of Korean version of falls efficacy scale-international (KFES-I). J Korean Acad Rehabil Med 2010; 34: 554–559.

34.

Park

Cho

Shin

J-H

, et al. Relationship among fear of falling, physical performance, and physical characteristics of the rural elderly. Am J Phys Med Rehabil 2014; 93: 379–386.

35.

Folstein

McHugh

. Mini-mental state”: a practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res 1975; 12: 189–198.

36.

Song

Lee

K-H

, et al. Development and validation of the full version of story memory in the Korean-mini mental state examination: Expanded version (K-MMSE-2: eV). Dementia Neurocognitive Disord 2019; 18: 96–104.

37.

Stoyanov

Hides

Kavanagh

, et al. Mobile app rating scale: a new tool for assessing the quality of health mobile apps. JMIR Mhealth Uhealth 2015; 3: e3422.

38.

Stoyanov

Hides

Kavanagh

, et al. Development and validation of the user version of the mobile application rating scale (uMARS). JMIR Mhealth Uhealth 2016; 4: e5849.

39.

Levine

Newmark

, et al. Design and testing of a mobile health application rating tool. NPJ Digital Med 2020; 3: –7.

40.

Wright

Cook

Baxter

, et al. A comparison of 3 methodological approaches to defining major clinically important improvement of 4 performance measures in patients with hip osteoarthritis. J OrthopSports Phys Ther 2011; 41: 319–327.

41.

Portenhauser

Terhorst

Schultchen

, et al. Mobile apps for older adults: Systematic search and evaluation within online stores. JMIR aging 2021; 4: e23313.

42.

Mansson

Pettersson

Rosendahl

, et al. Feasibility of performance-based and self-reported outcomes in self-managed falls prevention exercise interventions for independent older adults living in the community. BMC Geriatr 2022; 22: 147.

43.

Adcock

Fankhauser

Post

, et al. Effects of an in-home multicomponent exergame training on physical functions, cognition, and brain volume of older adults: a randomized controlled trial. Front Med (Lausanne) 2020; 6: 321.

44.

Luximon

. Older adults’ use of mobile device: Usability challenges while navigating various interfaces. Behav Inf Technol 2020; 39: 837–861.

45.

Landers

Oscar

Sasaoka

, et al. Balance confidence and fear of falling avoidance behavior are most predictive of falling in older adults: Prospective analysis. Phys Ther 2016; 96: 433–442.

46.

Geraedts

Dijkstra

Zhang

, et al. Effectiveness of an individually tailored home-based exercise programme for pre-frail older adults, driven by a tablet application and mobility monitoring: a pilot study. Eur Rev Aging Phys Act 2021; 18: –9.

47.

Rainero

Summers

Monter

, et al. The my active and healthy aging ICT platform prevents quality of life decline in older adults: a randomised controlled study. Age Ageing 2021; 50: 1261–1267.

48.

Kim

D-R

Song

Kim

G-M

, et al. Effects of ICT-based multicomponent program on body composition and cognitive function in older adults: a randomized controlled clinical study. Clin Interv Aging 2021; 16: 1161.