Technology Acceptance of a Mobile Application to Support Family Caregivers in a Long-Term Care Facility

Abstract

Background Family caregivers are unpaid individuals who provide care to people with chronic conditions or disabilities. Family caregivers generally do not have formal care-related training. However, they are an essential source of care. Mobile technologies can benefit family caregivers by strengthening communication with care staff and supporting the monitoring of care recipients.

Objective We conducted a mixed-method study to evaluate the acceptance and usability of a mobile technology called the Smart Care System.

Methods Using convenience sampling, we recruited 27 family caregivers to evaluate the mobile Smart Care System (mSCS). In the quantitative phase, we administered initial and exit questionnaires based on the Unified Theory of Acceptance and Use of Technology. In the qualitative phase, we conducted focus groups to explore family caregivers' perspectives and opinions on the usability of the mSCS. With the quantitative data, we employed univariate, bivariate, and partial least squares analyses, and we used content analysis with the qualitative data.

Results We observed a high level of comfort using digital technologies among participants. On average, participants were caregivers for an average of 6.08 years (standard deviation [SD] = 6.63), and their mean age was 56.65 years (SD = 11.62). We observed a high level of technology acceptance among family caregivers (7.69, SD = 2.11). Behavioral intention (β = 0.509, p-value = 0.004) and facilitating conditions (β = 0.310, p-value = 0.049) were statistically significant and related to usage behavior. In terms of qualitative results, participants reported that the mobile application supported care coordination and communication with staff and provided peace of mind to family caregivers.

Conclusion The technology showed high technology acceptance and intention to use among family caregivers in a long-term care setting. Facilitating conditions influenced acceptance. Therefore, it would be important to identify and optimize these conditions to ensure technology uptake.

 

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Keywords

Technology - Mobile Technologies - Caregivers - Older Adults - Long-Term Care - Usability - Evaluation - Mixed Methods

Background and Significance

As populations age,[1] [2] [3] the number of family caregivers continues to grow.[4] [5] [6] Globally, the estimated number of family caregivers has significantly increased since 2015.[7] [8] It was projected that around 13% of people aged 50 years and older provided informal care at least weekly in 2019.[3] [9] In the United States, nearly 42 million caregivers provide unpaid care to an adult with health or functional needs over the age of 50 years.[7] In Canada, almost 7.8 million people or 25% of the population are family caregivers.[4]

Family caregivers are relatives of people who have chronic health conditions or disabilities, including persons in long-term care (LTC) facilities.[10] These caregivers are unpaid, and most do not have formal care-related training.[4] [11] Family caregivers usually assist in activities of daily living (i.e., eating and dressing) and strengthen the care recipient's quality of life by providing social and emotional support.[9] [10] [12] [13] [14] [15] [16] [17] [18]

The family caregiver role can be time-consuming and challenging[4] [19] [20] and is commonly associated with stress and burden.[3] [9] [21] [22] Family caregivers can be involved in caring for more than one individual, for example, their mother and father.[4] [7] In some cases, they provide up to 30 to 43 hours of care per week.[4] [23] The total cost of caregiver activities is estimated at approximately 3% of the gross domestic product in Sweden, equivalent to 16.3 billion United States Dollars.[23]

Family caregivers of individuals living in LTC facilities provide essential care through daily care activities, emotional support, and advocacy.[24] Family caregivers visit their care recipient several times per week to provide direct care (i.e., bathing and toileting) and meals and manage and monitor the care recipient's quality of life.[24] [25] [26] Family caregivers communicate and interact with the facility staff[25] and have multiple communication needs, including obtaining care information.[27] [28] The unceasing monitoring of care activities can be demanding.[29] [30] [31] [32] [33]

Family caregivers of individuals living in LTC may be reluctant to contact staff to inquire about the status of their relatives because of the high workloads experienced by the LTC staff.[9] [13] [34] Mobile technologies, including smartphone applications, can facilitate communication with staff,[5] [35] [36] [37] [38] [39] [40] by supporting care coordination and remote care.[6] [41] [42] [43] [44] Mobile technologies can also enhance social interactions with the care recipient and increase the engagement of families in care activities.[30] [45] [46]

The adoption of technologies to support family caregivers remains low.[5] [47] [48] Reasons include lack of awareness of these technologies,[49] usability and privacy concerns,[50] [51] and technology limitations to address caregiver's needs and expectations.[47] [48] [51] Nevertheless, some caregivers do use technologies and experience benefits.[7] [51] [52] The coronavirus disease of 2019 (COVID-19) pandemic highlighted the potential of mobile technologies to support the communication and interactions of LTC recipients with family caregivers[53] and reduce isolation.[53] [54] [55] [56] [57] [58] Studies have identified that perceived usefulness and familiarity with the technology can promote the adoption of mobile technologies.[37] [59]

This paper is a subsequent phase of a more extensive study conducted to explore a mobile app's technology acceptance and usability. In the first phase, we evaluated the Mobile Smart Care System (mSCS) for use with health care aides who provide services to older adults.[60] The mobile application or mSCS, had two interfaces to serve different users, one for health care aides and nurse managers and a different one for family caregivers. We evaluated this technology in separate phases at distinct times. Consequently, this paper reports the study's second phase with family caregivers.

In this study, we employed mixed methods to evaluate the acceptance and usability of a mobile application which connects family caregivers to care recipients living in LTC. This study presented an innovative and accessible mobile application that can keep caregivers updated with their family members' care.

 

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Objectives

We customized a mobile application in collaboration with an LTC facility (i.e., Wing Kei Care Centre, or WKCC) and an electronic medical record company based in Edmonton, Alberta (i.e., Clinisys EMR). We aimed to evaluate the technology acceptance and usability of the mSCS, for family caregivers who care for persons living in LTC. This study used the Unified Theory of Acceptance and Use of Technology (UTAUT).[61] The UTAUT postulates that performance expectancy (PE), effort expectancy (EE), and social influence (SI) are direct determinants of one's behavioral intention (BI) to use the technology.[61] Facilitating conditions (FC) and BI to use the technology are the two determinants of usage behavior (UB).[62]

The Mobile Smart Care System

[Fig. 1] shows the mSCS architecture. The mSCS was customized to engage family caregivers in care activities and address family caregivers' communication and information needs. The mSCS is a module of Clinisys EMR system. It uses security standards, including strong-encryption and cryptography to make communications and interactions secure,[63] protecting participants against a release of personal and health information. The mSCS consists of a web-based mobile application that includes care plan observations of activities such as eating, mobility, recreation, and care recipients' emotional states. The mSCS was accessed using an internet browser on smartphones and computers.

Fig. 1 Mobile Smart Care System architecture.

 

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Methods

Study Design

This study used a sequential explanatory mixed-methods approach[64]; in the quantitative phase, we used pre- and postpaper-based questionnaires with no control group. We also conducted two focus groups with a subsample of the family caregivers toward the end of the study, thus a qualitative descriptive design.

 

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Setting, Participants, and Recruitment

The study was conducted at one of three locations of WKCC which is a faith-based LTC facility that provides culturally specific programs and services to Chinese older adults and utilizes a person and family-centered care framework.[65] Family caregivers were recruited in collaboration with the facility using convenience sampling.

For the quantitative component, we followed Hair et al[66] suggestion to estimate the target sample size of 30 participants and estimated the power analysis for correlation using Portney and Watkins'[67] procedure, suggested to achieve a statistical power of 0.80 with an effect size of 0.25 and an alpha of 0.05 for a partial least square (PLS) structural regression models.

A total of 46 family caregivers were invited to participate. The study coordinator (H.P.) sent the study information and invitation letter and followed up with prospective participants via phone and email; ultimately, 27 participated. Some potential participants expressed a lack of time to participate. Aside from being familiar with mobile technologies such as smartphones and tablets and having access to these, there were no other inclusion criteria for family caregivers. Once they agreed to participate, they completed a consent form.

 

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Variables

Outcome variables were BI and UB. These were used in the multivariate PLS model to determine what factors influenced the acceptance and use behavior of the mSCS.[61] PE, EE, and SI were independent variables determinants of BI. FC and BI were considered determinants of UB. We collected potential data on confounding variables which included demographic data: age, sex, family caregiver relationship to the care recipient, duration of caregiving in years; and participants' level of comfort using digital technologies.

 

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Data Sources and Measurement

Technology Acceptance and Usability Questionnaires

We designed and administered a 10-item paper-based initial questionnaire based on two items per UTAUT construct. The 12-item exit questionnaire had two added questions for UB with the mSCS. The purpose of the initial questionnaire was to obtain initial expectations of acceptance of the mobile system. The exit questionnaire examined actual acceptance and UB and whether the mSCS met family caregivers' expectations.

The questionnaires had three sections: (1) demographics, (2) family caregivers' level of comfort using digital technologies, and (3) evaluation of the UTAUT constructs. [Table 1] shows the UTAUT constructs with the measurement items used in questionnaires. Items were rated on a five-point Likert scale ranging from “strongly disagree (1)” to “strongly agree (5)”. These items have demonstrated internal consistency in the UTAUT model in previous studies.[60] [61] [68] [69]

Table 1 Summary constructs and measurement items

Construct	Corresponding items (initial questionnaire)	Related items (exit questionnaire)	Source
Performance Expectancy (PE)	PE1. Using the mSCS will improve the management of the caregiving duties for the family member/friend under my care.	PE1. Using the mSCS improved the care I provided to the family member/friend under my care.	Venkatesh and Davis[61]
	PE2. Overall, the mSCS will be useful to my caregiving responsibilities.	PE2. Overall, the mSCS was useful for my caregiving responsibilities.	Venkatesh and Davis [61]
Effort Expectancy (EE)	EE1. Learning to use the mSCS will be easy for me.	EE1. Learning to use the mSCS app was easy for me.	Venkatesh and Davis [61]
	EE2. Overall, I will find the mSCS easy to use.	EE2. Overall, the mSCS was easy to use.	Liu et al[68]
Social Influence (SI)	SI1. People who are important to me think that I should use the mSCS to manage my caregiving activities.	SI1. My colleagues think that I should use the mSCS to manage my caregiving activities.	Liu et al[68]
	SI2. In general, my family members or friends will support use of the mSCS to manage my caregiving duties.	SI2. In general, my supervisor supported my use of the mSCS to manage my caregiving activities.	Liu et al[68]
Facilitating Conditions (FC)	FC1. I will receive good technical support with using the mSCS.	FC1. I received good technical support for using the mSCS.	Liu et al[68]
	FC2. The mSCS will be fast to get into.	FC2. The mSCS was fast to get into.	Liu et al[68]
Behavioral Intention (BI)	BI1. If possible, I will use the mSCS to manage my caregiving activities.	BI1. If it were up to me, I would continue to use the mSCS.	Liu et al[68]
	BI2. If possible, I will continue to use the mSCS app to provide a better service to my clients.	BI2. If it were up to me, I would continue to use the mSCS to provide better care to family members or friend under my care.	Liu et al[68]
Usage Behavior (UB)	Not applicable	UB1. I used the mSCS to organize my caregiving activities.	Liu et al[68]
	Not applicable	UB2. I used the mSCS to manage my caregiving activities.	Liu et al[68]

 

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Operationalization of Variables

Dichotomous variables such as caregiver sex were coded “0” or “1”. Each item in the third section of the questionnaire was related to each dependent and the independent latent variables. Consistent with previous studies,[60] [70] we calculated three summative scores by adding: (1) the items from the UTAUT constructs (except for UB, 10 items), (2) the items from each UTAUT construct (two items), and (3) the items from the level of comfort using digital technology (four items).

Based on the 5-point Likert scale, the maximum possible value for the first summative score was 50 points (two items for five UTAUT constructs). Thus, a summative score higher than 30 and closer to 50 points suggests high technology acceptance of the mSCS. The maximum possible value for the second summative score was 10 points (two items per construct). Therefore, a summative score higher than six points and closer to 10 points would suggest that PE, EE, SI, FC, and BI to use the mSCS were high. Finally, for the third score, a summative score higher than 12 points and closer to 20 points suggests that family caregivers have high comfort levels with digital technologies.

 

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Focus Groups

The focus group guides contained six questions that explored family caregivers' experiences using the mSCS (i.e., ease of use, participants' satisfaction with the app, impact on caregivers' activities, influence on the caregiver role), and how the mSCS could support quality of care and using the system in a home care environment versus in LTC facilities.

 

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Procedures

The study coordinator (H.P.) administered the initial questionnaire. Next, participants watched a video explaining how to use the mSCS and were given their usernames and passwords to log into the system. Participants used the system for 1 month. The testing period occurred from November 15, 2021, to January 7, 2021.

Afterward, participants completed the exit questionnaire administered by the study coordinator. Participants received an honorarium ($25.00 CAD) for completing the initial questionnaire and another honorarium of the same value for completing the exit questionnaire. Quantitative data collection occurred during the testing period, it started on November 15, 2021, and finished on January 7, 2022. Participants took approximately 30 minutes to complete each questionnaire. Most participants completed the initial questionnaire in person and the exit questionnaire remotely (i.e., the participant received a file with the questionnaire and returned it), due to the COVID-19 measures in place for physical distancing at the time of the data collection.

We conducted two online focus groups using Zoom toward the end of the study with a subsample of participants. Sixteen family caregivers participated, nine in the first group and seven in the second. The focus group size was sufficient for the study scope and design.[71] [72] Four researchers (C.D., A.M.C., H.P., and E.R.) conducted and video and audio recorded the focus groups and transcribed verbatim. Before each focus group, participants signed a consent form. Each session included three interviewers (C.D., A.M.C., and H.P.) and one notetaker (E.R.), C.D. facilitated the session. Questions that guided the focus groups were aligned with the items of the initial and exit questionnaires and based on the previous phase of this study.[60] Each focus group session lasted 1 hour and participants received an honorarium ($25.00 CAD). Both sessions occurred on December 17, 2021, at 10:00 AM and 1:00 PM, respectively.

 

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Data Analyses

We used descriptive statistics to summarize demographic data. Comparisons of the outcome and independent variable summative scores at the pretest and posttest were calculated using a paired t-test. We tested the normality of the constructs using the Kolmogorov–Smirnov[73] and Shapiro–Wilk[74] tests (see [Supplementary Appendix], [Table B1], available in the online version). The pretest results are reported in the [Supplementary Appendix], [Tables B2]–[B4] (available in the online version).

We used the posttest results to calculate the correlations, construct reliability and validity, measure the PLS model, and estimate the determinants of BI and UB regarding the mSCS. The pretest results are reported in the [Supplementary Appendix], [Tables B2]–[B4] (available in the online version). During the PLS model calculation, we observed that item EE2 of the UTAUT constructs was below the recommended parameters for loading values.[75] Hence, we followed Diamantopoulos et al[76] and Hair et al[66] [77] suggestion for single-items that can be left out without changing the meaning of the construct[66] [78] and should be considered only when the sample size is small or less than 50, and low path coefficients or lower than 0.30 are expected.[66]

To determine whether to include a moderator and mediator variables in the PLS model, we calculated bivariate correlations (Spearman's rho or Pearson's correlation) between PE, EE, SI, and BI to UB, and the current use of the mSCS was independent of sex, age, level of comfort using mobile applications, and years of experience as a caregiver (see [Supplementary Appendix], [Table B5], available in the online version).

We evaluated the PLS model through individual construct reliability (i.e., internal composite reliability) and convergent validity measurements of each set of items related to their associated construct, assessed by examining each item's factor loading according to the PLS model constructs.[66] First, we analyzed the discriminant validity using the average extracted variance (AVE). Next, the PLS model was evaluated utilizing path coefficients (β), the explained variance (R² ) and the effect size (f² ) for each path segment of the model. Path coefficients were interpreted as regression coefficients with the t-statistic calculated using bootstrapping,[15] a nonparametric technique for estimating the precision of PLS model estimates. Finally, we examined the significance of the path coefficients. Each test's alpha level of significance was set at p ≤ 0.05 (two-tailed). We used statistics packages SPSS (Version V 28.0) and SmartPLS (Version 3.2.0)[79] to generate descriptive, univariate, and bivariate statistics and a PLS structural regression model. The sample size calculations were estimated using G*Power (Version 3.1.9.6).[14]

For the qualitative analysis, we used conventional content analysis[80] to identify codes and categorize them.[81] A researcher (E.R.) coded and analyzed the data by listening to the recordings while reading the transcripts and noting initial reflections. Then identified and coded transcript segments using keywords. Codes were described, and similar codes were grouped and refined to create categories. Category descriptions were generated to articulate their contents and relationships within. Finally, a code hierarchy was created for each transcript and combined to create a hierarchy.

A second researcher (C.D.) reviewed the codes against the transcripts, the coding framework, and the accompanying descriptions. Discrepancies were discussed and resolved following peer debriefing, which enabled scrutinizing codes to enhance internal and external homogeneity.[81] The qualitative analysis was subsequently reviewed, discussed, scrutinized, and confirmed during research meetings.

 

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Results

Participants

Forty-six family caregivers were invited to participate in this study, and twenty-seven completed the initial questionnaire. One participant dropped out before completing the exit questionnaire, without providing additional details on his decision. Thus, the final sample comprised 26 family caregivers, representing 96.3% (26 out of 27) participants.

Description of the Family Caregivers

[Table 2] summarizes participant demographics. The average age was 57 years. Females and males were equally represented. Participants had almost 6 years of experience as caregivers. Most provided care for their mothers and fathers and were comfortable using digital technologies.

Table 2 Demographics and comfort using digital technologies

	Quantitative phase (n = 26)	Qualitative phase (n = 16)
Years as caregiver
Mean (SD)	6.08 (6.63)	16 (7.64)
Family caregiver's age
Range, mean (SD)	29 to 70, 56.65 (11.62)	36 to 68, 60.44 (7.9)
Sex	n (%)	n (%)
Male	11 (42.3)	6 (37.5.4)
Female	15 (57.7)	10 (62.5)
The care recipient is	n (%)	n (%)
Mother	11 (42.3)	8 (50)
Father	7 (26.9)	5 (31.3)
Grandmother	5 (19.2)	1 (6.3)
Brother	1 (3.8)	1 (6.3)
Grandparent	1 (3.8)	1 (6.3)
Great grandmother	1 (3.8)	–
Level of comfort using digital technologies
	Mean
I am comfortable using a computer	5	5
I am comfortable using a tablet	5	5
I am comfortable using a smartphone	5	5
I am comfortable using the internet	5	5
Summative score[a]	20	20

Abbreviation: SD, standard deviation.

^a Summative score (min, max) = (4, 20). Values ranged from 1 = strongly disagree to 5 = strongly agree.

 

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Acceptance and Usability of the Mobile Smart Care System: Quantitative Results

[Tables 3] and [4] show the descriptive statistics and hypothesis tests (paired t-tests) of the technology acceptance for the mSCS in terms of a summative score based on all the UTAUT construct items) and for each UTAUT construct, respectively.

Table 3 Family caregivers' level of technology acceptance on initial and exit questionnaires

	Initial questionnaire (n = 26)	Exit questionnaire (n = 26)	Paired t-test statistics
	Mean (SD)	Mean (SD)	p-Value	t (df)	95% CI	Effect size	Power (%)
Summative score[a]	42.57 (5.41)	45.57 (4.68)	0.034	−2.25 (25)	(−5.74, −0.25)	0.64	88%

Abbreviations: CI, confidence interval; SD, standard deviation; t (df), t-value and degrees of freedom.

^a Min. summative score = 10, Max. summative score = 50. All the UTAUT construct items.

Table 4 Family caregivers' level of technology acceptance on initial and exit questionnaires

	Initial questionnaire[a] (n = 26)	Exit questionnaire[a] (n = 26)	Paired t-test statistics
Construct	Mean (SD)	Mean (SD)	Mean differences	p-Value	t (df)	95% CI	Effect size	Power (%)
PE	9 (1.17)	9.08 (1.32)	−0.08	0.821	−0.23 (25)	(−0.77, 0.62)	0.09	7.26
EE	8.19 (1.44)	9.58 (0.90)	−1.38	<0.001[b]	−4.88 (25)	(−1.97, −0.80)	2.18	100
SI	8.31 (1.44)	8.54 (1.56)	−0.23	0.581	−0.56 (25)	(−1.08, 0.62)	0.21	17.76
FC	8.08 (1.49)	8.85 (1.29)	−0.77	0.032[b]	−2.27 (25)	(−1.47, −0.07)	0.85	98.62
BI	9 (1.13)	9.54 (0.99)	−0.54	0.07	−1.89 (25)	(−1.12, 0.05)	0.77	96.50
UB	–	7.69 (2.11)	–	–	–	–	–	–

Abbreviations: BI, behavioral intention; CI, confidence interval; EE, effort expectancy; FC, facilitating conditions; PE, performance expectancy; SD, standard deviation; t (df), t-value and degrees of freedom; SI, social influence; UB, usage behavior.

Notes:

^a Values ranged from 1 = strongly disagree to 5 = strongly agree, two items per UTAUT construct. Min. summative score = 2, Max. summative score = 10.

^b Statistically significant.

[Table 3] shows that the acceptance of the mSCS was high. A summative score over 40 indicates that family caregivers' expectations of the mSCS were met. We also observed a difference between the initial and exit summative scores. This result suggests that family caregivers' acceptance of the mSCS increased after the testing period, and this difference was statistically significant.

Regarding the results for each UTAUT construct, family caregivers believed the mSCS was useful (high PE) and was a good fit with participants' needs (high FC), and the influence of the other people on their use was strong (high SI). Notably, participants would be willing to use the mSCS in the future (average intention to use the system, BI construct = 9.54; standard deviation (SD) 0.99). With the exit questionnaires, we observed that based on its summative score, the value obtained for usability levels was high (average UB with the mSCS = 7.69 SD 2.11). The differences between the initial and exit summative scores for EE and FC were statistically significant (see [Table 4]).

Validity and Reliability Using the Partial Least Square Model

[Table 5] shows that the results of the construct correlations, descriptive statistics, Cronbach's alpha, internal composite reliability, and the AVE of the PLS model constructs (the square root of each AVE) were greater than the related interconstruct correlations in the construct correlation matrix, indicating adequate discriminant validity for all the constructs.[66]

Table 5 Construct correlations and construct reliability and validity of the PLS model (n = 26)

Construct	Mean[a] (SD)	ICR	CA	AVE[b]	BI[c]	EE[c]	FC[c]	PE[c]	SI[c]	UB[c]
BI	9.54 (0.99)	0.92	0.82	0.85	0.92
EE	9.58 (0.90)	1.00	1.00	1.00	0.64	1.00
FC	8.85 (1.29)	0.76	0.44	0.62	0.49	0.60	0.79
PE	9.08 (1.32)	0.93	0.84	0.86	0.53	0.31	0.62	0.93
SI	8.54 (1.56)	0.91	0.82	0.83	0.43	0.33	0.44	0.62	0.91
UB	9.08 (1.32)	1.00	1.00	1.00	0.66	0.30	0.56	0.52	0.28	1.00

Abbreviations: AVE, average extracted variance; BI, behavioral intention; CA, Cronbach's alpha; EE, effort expectancy; FC, facilitating conditions; ICR, internal composite reliability; PE, performance expectancy; PLS, partial least square; SD, standard deviation; SI, social influence; UB, usage behavior.

Notes: Square root of AVEs reported along diagonal in bold (Fornell–Larcker criterion).

^a “1” strongly disagree to “5” strongly agree, two items per UTAUT construct min. score = 2, max. summative score = 10.

^b All the AVE values were greater than 0.5, indicating good levels of convergent validity at the construct level.[66] [75] Nearly all the ICR and CA values were greater than 0.70, indicating good internal consistency at the construct level.[66] [75]

^c Fornell–Larcker criterion.

The PLS model indicated that most of the item's loadings were statistically significant at the 0.001 level. Also, virtually all items' loadings were greater than 0.70, indicating excellent convergent validity values at the indicator level[66] [75] (see [Table 6]). The explained variance (R²) of the constructs of the PLS model was 0.536 and 0.511 for BI to use the technology and actual UB with the mSCS, respectively, which appears to be strong according to the published criteria.[66] [75]

Table 6 Reliability and convergent validity of the PLS model—measurement model (n = 26)

Construct	Item	Item loading	T Statistics	95% CI	ICR	AVE	CA
PE	PE1	0.93	15.16	(0.75, 0.98)	0.93	0.86	0.84
	PE2	0.93	17.58	(0.82, 0.97)
EE	EE1	1.00	–[a]	(1, 1)	1.00	1.00	1.00
SI	SI1	0.97	17.54	(0.87, 0.99)	0.91	0.83	0.82
	SI2	0.85	7.85	(0.65, 0.95)
FC	FC1	0.93	8.56	(0.71, 0.99)	0.76	0.62	0.44
	FC2	0.62	2.65	(0.02, 0.91)
BI	BI1	0.92	14.60	(0.87, 1)	0.91	0.84	0.82
	BI2	0.92	9.47	(0.70, 1)
UB	UB1	1.00	–[a]	(1, 1)	1.00	1.00	1.00

Abbreviations: AVE, average extracted variance; BI, behavioral intention; CA, Cronbach's alpha; CI, confidence interval; EE, effort expectancy; FC, facilitating conditions; ICR, internal composite reliability; PE, performance expectancy; PLS, partial least square; SI, social influence; UB, usage behavior.

^a Not applicable. One item of the constructs was eliminated during the analysis because its loading value was below recommended parameters.[75]

 

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Acceptance and Usability of the Mobile Smart Care System: Multivariate Analyses (Partial Least Square Model)

In general, we found statistically significant differences only between the initial and exit summative scores of the UTAUT constructs for EE and FC ([Table 4]). Therefore, we only ran one PLS model (the posttest model). The bivariate analysis showed that participants' responses to PE, EE, SI, FC, BI, and UB with the mSCS were independent of sex, age, comfort level using digital technologies, and years of experience as a caregiver (see [Supplementary Appendix], [Table B5], available in the online version). Thus, the variables should not be treated as confounders or moderator variables in the PLS analysis modeling.

[Table 7] shows the results from the PLS model. We obtained positive correlations between PE to BI (PE → BI, β = 0.338, p = 0.407) and EE and BI (EE → BI, β = 0.515, p = 0.255), although they were not statistically significant. Similarly, we found that SI was not a salient construct for BI (SI → BI, β = 0.057, p = 0.692). Conversely, we also found a positively strong and statistically significant correlation between BI and UB (BI → UB, β = 0.509, p = 0.004). We also found FC and UB had a positive and statistically significant correlation (FC → UB: β = 0.310, p = 0.049).

Table 7 Determinants of BI and UB regarding the mSCS

Path	Family caregivers (n = 26)
	β[a]	T Statistics	p-Value	95% CI	Effect size	R 2 [b]	R 2 adjusted[c]	Power %
PE → BI	0.338	0.83	0.407	(−0.03, 1.27)	0.14	0.536	0.476	100
EE → BI	0.515	1.14	0.255	(−0.57, 0.87)	0.49
SI → BI	0.057	0.39	0.692	(−0.30, 0.25)	0.00
BI → UB	0.509	2.87	0.004[d]	(0.05, 0.77)	0.40	0.511	0.471	100
FC → UB	0.310	1.96	0.049[d]	(0.05, 0.70)	0.15

Abbreviations: BI, behavioral intention; CI, confidence interval; EE, effort expectancy; FC, facilitating conditions; mSCS, mobile Smart Care System; PE, performance expectancy; SI, social influence; UB, usage behavior.

^a Path coefficients.

^b Explained variance.

^c Adjusted R-squared.

^d Statistically significant.

 

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Acceptance and Usability of the Mobile Smart Care System: Qualitative Results

Sixteen family caregivers participated in two focus groups. Three key themes emerged: (1) the mSCS was easy to use, (2) it has the potential to improve communication between family caregivers and staff, and (3) it could give family caregivers peace of mind ([Supplementary Appendix A]).

First, participants observed the ease of use of the mSCS since it was easy to use and “user friendly” (FCG1P2), referring to the ability to access regular updates (FCG1P8) on the mSCS from anywhere on any device (FCG1P7) and the simple design made it easy to use. Participants said accessing the information on the mSCS system was “fast, easy, and quick.” (FCG1P1).

Second, participants stated that the mSCS has the potential to improve communication between family caregivers and LTC staff. For example, regular updates about the care recipient's health and care plans were essential to family caregiver participants, and the mSCS could efficiently provide information: “I especially don't like calling the […] because I understand how busy the staff are all the time. So, I hesitate to […] this app will be very helpful, if the information is uploaded on a regular basis” (FCG2P3).

Additionally, family caregivers described that the mSCS gave them peace of mind by providing updates on their care recipients and being a way of direct communication with the LTC facility. However, they reported that use of the system would not change their responsibilities. “In terms of changing the role, no, because we still do basically everything that we normally do. But what it did for me […] probably relieve a little bit more anxiety… [give me] peace of mind” (FCG2P6). Another participant observed how the mSCS “enhances as a primary caregiver, … I have the information readily coming from the institution to the family, without me interpreting or doing anything to it and they can see it for themselves” (FCG2P5).

Finally, participants suggested improving the staff notes (i.e., more specific information) and enabling historical data, such as frequency of exercise, moods, and vital signs (i.e., blood pressure and weight), and updates daily versus occasionally could increase the mSCS use by family caregivers. Additional features to improve the mSCS suggested by participants include adding a calendar functionality, so family caregivers can navigate back in time and track care recipient's status and adding different languages. The focus group results are included as [Supplementary Material] (available in the online version).

 

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Discussion

In this study, we aimed to investigate the acceptance and usability of a mobile application in an LTC facility used by family caregivers who provide care to older adults. We collaborated with industry and health care partners to customize and evaluate the mSCS. Twenty-six family caregivers used the technology for over a month. Our results confirm that the mSCS acceptance and usability were high, with statistically significant differences between the initial and exit questionnaires, implying that the system's acceptance improved after the testing period. This study was part of a larger project. In the first phase, we evaluated the mSCS acceptance and usability with health care aides who provide services to older adults. They used a different interface of the mSCS, customized to support their workflows.[60] Evaluations with each group of participants occurred at distinct times, sequentially.

This evaluation showed that BI (i.e., caregivers' intention to use the mSCS) was a statistically significant predictor of UB. This result is consistent with previous studies that identified caregivers' BI as a predictor of UB.[68] [70] [82] [83] [84] For instance, Dai et al[85] found that BI was a strong predictor of UB of wearable devices among family caregivers of persons living with dementia. Namely, family caregivers would use the mSCS because their intention to use the system was high, as the UTAUT theory posits.[61]

Similarly, we found that FC (i.e., the degree to which a person believes the existing infrastructure can support the use of technology)[86] had a positive and statistically significant relationship with UB for family caregivers. In other words, participants identified they had the resources and skills to use the mSCS (i.e., access to the Internet and technology literacy). These findings can be explained by their previous experiences and access to mobile technologies[87] and the participants' high level of comfort using digital technologies. FC are referred as a predictor of UB of technologies for caregivers.[61] [68] [85] [87] Similarly, Adjei et al[88] found FC and BI positively and significantly related to UB in a study exploring a mobile technology intended to support caregivers.

Neither PE, EE, nor SI was statistically significant constructs for BI. In other words, caregiver participants would use the mSCS regardless of how effective or easy it was to use the mSCS, or the extent of family influence they have to use the mSCS.[61] [68] A possible explanation is that they want to receive updates and stay connected with the care recipient, as suggested during the qualitative phase of the study and in similar studies with family caregivers.[89] [90] [91] Mendez et al[92] also reported that neither EE nor SI were related to BI among caregivers of persons living with dementia. However, these authors observed the importance of considering EE when implementing technologies because EE was significantly associated with greater BI to use technology. These results are similar to previous studies, where high BI and FC are the only predictors of UB.[88]

We observed that participants of this evaluation wanted to be updated on the care recipients' health and recreational engagement and involved in care coordination and communication with LTC staff. Similarly, during the qualitative phase, participants highlighted that having technologies such as the mSCS could engage more family members in care activities. This concern is particularly relevant for caregivers who live in different cities than the care recipient.[30] [34] [93] [94]

Results from the first phase of the study releveled that among health care aides, PE or the degree to which an individual believes that using a system will help him or her to attain gains in job performance[61] was the strongest predictor of intention to use the mSCS.[60] Health care aides also reported that the mSCS was useful and easy to use.[60] The same was reported by family caregivers, who also found the mSCS easy to use.

Although during the first phase, the study participants used a different interface of the mSCS intended to support their workflows, and each evaluation occurred at distinct times, we found that BI was a strong predictor of UB (i.e., UB) for both groups of participants.[60] These results could be explained by the fact that health care aides want technology to support their workflows[60] and family caregivers, on the contrary, require technology to strengthen communication with care staff and support the monitoring of care recipients.

Family caregivers and care recipients in LTC facilities experienced the impacts of the COVID-19 pandemic, such as physical contact restrictions and limited interactions.[53] [55] [95] [96] [97] [98] As such, the pandemic accelerated the adoption and diffusion of new technologies in health care.[18] Mobile technologies can minimize the risk of exposure to COVID-19 and serve as alternative ways to communicate with the care recipient and LTC staff.[99] [100] Family caregivers observe the advantages of technologies to support their roles,[97] [101] [102] [103] [104] such as reducing caregiver stress by providing information,[97] supporting the mental and physical health of care recipients, and reducing and promoting interaction with LTC staff.[105]

The demand for effective technologies to support caregivers will become more prominent.[51] Evidence suggests that family caregivers would adopt technologies in their care activities,[106] and this adoption would be supported by the nurse and LTC staff before large implementation.[60] [107] Thus, the design and implementation of mobile technologies should consider addressing caregiving needs and expectations,[108] which is critical because users have high expectations of new health care technologies.[109] [110]

Future research directions include studies that (1) measure the impact of health care applications on care recipient and family caregivers' quality of life, (2) evaluate the impact of healthcare technologies on the care recipients' overall health (i.e., physical, social, and mental); and (3) examine the influence of technology literacy on levels technology acceptance and usability among family caregivers.

In summary, this study demonstrated that family caregivers had high acceptance of mSCS, and two factors predicted its acceptance and use: BI and FC. The findings suggest that the mSCS facilitates interactions and communications between family caregivers and staff in an LTC facility. Understanding and evaluating the factors that facilitate the use and adoption of technologies are crucial to integrating new technologies to support caregivers.[100] [111]

 

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Limitations

Although we obtained most of the variables in the expected direction predicted by UTAUT, some were not statistically significant. This could be attributed to the relatively small sample size, which may also explain some pre- and posttest results obtained a lower statistical power than the conventional 0.80 cut-off value. Despite efforts to recruit participants, we acknowledge the difficulties that family caregivers face in getting involved in research, more so during the COVID-19 pandemic outbreaks. Thus, future studies should pursue higher sample sizes when the effect sizes are small. This study was conducted in only one LTC facility, and we set inclusion criteria for participants. Hence, we caution against generalizing results to family caregivers of care recipients in other LTC facilities. Ideally, future studies would include multiple sites and broader inclusion criteria. We relied on self-reporting and did not track the actual use of the mSCS. Thus, future evaluations should consider monitoring the actual use. We did not include a control group of participants that used traditional communication methods such as telephone communication. A comparison between groups could strengthen the design of a future study. Likewise, participants were motivated to participate in this study because of the opportunity to receive information about a family member. This was a potential source of response bias. Finally, we observed a ceiling effect; most of the values obtained for the constructs approached the upper limit of the questionnaires. Thus, a seven-point Likert scale in technology acceptance and usability studies would provide a larger range of responses. Despite limitations, the findings reported in this study can inform future interventions.

 

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Conclusion

The mobile Smart Care System showed a high technology acceptance and intention to use among family caregivers in an LTC setting. Facilitating conditions influenced acceptance. The technology was seen as a tool to improve communication and interaction between family caregivers and LTC staff. Family caregivers observed the potential of the mSCS to support the monitoring and coordination of care for their care recipients. Therefore, it would be important to identify and optimize these conditions to ensure technology uptake.

 

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Clinical Relevance Statement

Family caregivers play an essential role as care providers for older adults and persons living with dementia. Technologies can support their activities and enhance their communication and interactions with LCT staff. Our findings suggested BI and facilitating conditions predicted the use of technologies for family caregivers. Caregivers would continue to use the technologies because they found them easy to use and wanted to receive updates and stay connected with their family member care recipient.

 

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Multiple Choice Questions

1. Which construct relationships were statistically significant with Usage Behavior?

   • SI and BI to UB

   • PE and SI to UB

   • BI and FC to UB

   Correct Answer: The correct answer is option c, BI and FC to UB.

2. What is a description of Facilitating Conditions?

   • The degree of ease associated with the use of the technology

   • The degree to which an individual perceives the mSCS will help him or her to improve an activity

   • The degree to which a person believes the existing organizational and technical infrastructure can support the use of technology

   Correct Answer: The correct answer is option c, the degree to which a person believes the existing organizational and technical infrastructure can support the use of technology.

 

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Conflict of Interest

None declared.

Acknowledgments

We would like to acknowledge our partners (Clinisys EMR and Wing Kei Care Centre), funders (Alberta Innovates and The Centre for Aging + Brain Health Innovation), and the participants in this study.

Author Contributions

A.M.C. and L.L. led the overall design of the evaluation. C.D. also contributed to the plan. E.R. and A.C. conducted the data analysis supervised by A.M.C. and H.P. H.P., C.D., A.M.C., and E.R. led the data collection and implementation of the evaluation. H.P. and A.M.C. drafted the manuscript, and C.D., E.R., S.,K, A.C., and L.L. edited and contributed to the manuscript. L.L. was the principal investigator and the grant holder of this study.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed and approved by the University of Alberta Institutional Review Board, protocol no.: Pro00095093.

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Publikationsverlauf

Eingereicht: 12. Juni 2022

Angenommen: 15. Oktober 2022

Accepted Manuscript online:
18. Oktober 2022

Artikel online veröffentlicht:
21. Dezember 2022