Background
Constructs | Definition | Advantages/concerns in e-health platforms |
---|---|---|
Consumer Engagement | “Customer engagement (CE) is a psychological state that occurs by virtue of interactive, co-creative customer experiences with a focal agent/object (e.g., a brand) in focal service relationships.” [14:260] “Consumer engagement is a multidimensional concept comprising cognitive, emotional, and/or behavioral dimensions, and plays a central role in the process of relational exchange where other relational concepts are engagement antecedents and/or consequences in iterative engagement processes within the brand community [15:107] | |
Consumer Satisfaction | “Consumer satisfaction is a response (emotional or cognitive); 2) the response pertains to a particular focus (expectations, product, consumption experience, etc.); and 3) the response occurs at a particular time (after consumption, after choice, based on accumulated experience, etc.) [20] “Satisfaction is defined as a global evaluation or feeling state” [21:256] | |
Perceived benefit | “Perceived benefit refers to the perceived likelihood that taking a recommended course of action will lead to a positive outcome, such as reduced risk or reduced worry” [24:36] “Benefits refer to the expected or experienced positive consequences of [a given behavior]” [25:50] “Perceived benefits construct is […] defined as an individual’s belief that specific positive outcomes will result from a specific behavior” [26:88] | |
Perceived Technological Risk | “(…) is commonly thought of as felt uncertainty regarding possible negative consequences of using a product or service” [29:453] “(…) the potential for loss in the pursuit of a desired outcome of using an e-service” [29:454] | Reliability of health services—The level of security of the patient’s clinical data and their correct storage on the web increases the level of reliability of the health services provided through the digital health platform [30] |
Effort Expectancy | “(…) is defined as the degree of ease associated with the use of the system” [31:509] | Intention to use—The patient’s effort expectancy affects the intention to use a digital health service [28] |
Perceived Usefulness | “The prospective user’s subjective probability that using a specific application system will increase his or her job performance within an organizational context’’ [32:985] “(…) the degree to which a person believes that using a particular system would enhance his or her job performance” [33:320] | Positive attitude of patients—The perception of usefulness of digital health services favorably predisposes the patients and facilitates the doctor’s decision-making process (medical decision making) [34] |
Perceived Ease of Use | “(…) the degree to which an innovation is perceived as being difficult to use [35:195] “(…) the degree to which a person believes that using a particular system would be free of effort” [33:320] |
Methods
Data collection procedure
Constructs | Authors | N. items | μ | DS | Variance | Min index | Max index | Alpha di Cronbach | |
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Cognitive engagement | Hollebeek et al. (2014) [51] | 3 | 4.65 | 1.97 | 3.88 | 4.52 | 4.74 | 0.93 | |
Emotional engagement | Hollebeek et al. (2014) [51] | 3 | 4.95 | 1.73 | 3.00 | 4.57 | 5.39 | 0.91 | |
Behavioural engagement | Hollebeek et al. (2014) [51] | 3 | 4.29 | 1.79 | 3.22 | 3.66 | 4.66 | 0.81 | |
Satisfaction | Wang et al., 2004 [52] | 3 | 5.49 | 1.43 | 2.07 | 5.42 | 5.60 | 0.90 | |
Perceived benefit | Win et al. (2016) [26] | 4 | 4.31 | 1.82 | 3.31 | 4.05 | 4.67 | 0.92 | |
Perceived Technological risk | Chen and Aklikokou (2020) [53] | 3 | 2.98 | 1.61 | 2.61 | 2.93 | 3.03 | 0.83 | |
Effort Expectancy | Venkatesh et al. (2012) [54] | 4 | 3.34 | 1.60 | 2.56 | 3.09 | 3.52 | 0.81 | |
Perceived usefulness | Davis et al. (1989) [33] | 4 | 5.29 | 1.55 | 2.41 | 5.11 | 5.56 | 0.93 | |
Perceived ease of use | Davis et al. (1989) [33] | 4 | 5.53 | 1.39 | 1.94 | 4.95 | 5.79 | 0.90 |
Statistical analyses
Results
Descriptive analysis
Variable | N | Percentage |
---|---|---|
Age | ||
Below 25 | 2 | 1.7 |
25 – 35 | 22 | 18.5 |
36 – 45 | 22 | 18.5 |
46 – 55 | 39 | 32.8 |
56 – 65 | 20 | 16.8 |
Above 65 | 14 | 11.8 |
Gender | ||
F | 74 | 62.2 |
M | 45 | 37.8 |
Profession | ||
Student | 2 | 1.7 |
Worker | 86 | 72.3 |
Unemployed | 9 | 7.6 |
Retired | 18 | 15.1 |
Housewife | 4 | 3.4 |
Are you a patient with a suspected or ascertained COVID-19 infection? | ||
No | 40 | 33.6 |
Yes | 64 | 53.8 |
Perhaps | 15 | 12.6 |
Why are you using the home telemonitoring service ‘paginemediche.it’ | ||
For easy access to health information that could help me to prevent illnesses | 24 | 20.2 |
For speedy access to health services | 6 | 5.0 |
To have access to the treatment needed for the cure | 10 | 8.4 |
To monitor my health status post-COVID-19 | 44 | 37.0 |
Other | 35 | 29.4 |
How long have you been using the COVID-19 home telemonitoring platform ‘paginemediche’? | ||
Less than a week | 0 | 0 |
A week | 17 | 14.3 |
Two weeks | 21 | 17.6 |
Three weeks | 15 | 12.6 |
A month | 16 | 13.4 |
More than a month | 50 | 42 |
Factor | ||||||
---|---|---|---|---|---|---|
1 | 2 | 3 | 4 | 5 | 6 | |
Health self-engagement | Technological risk | Perceived ease of use | Satisfaction | Patient cognitive engagement | Perceived usefulness | |
PB1 | .871 | |||||
ENGe2 | .848 | |||||
PB3 | .809 | |||||
ENGe3 | .799 | |||||
PB4 | .764 | |||||
ENGb2 | .757 | |||||
ENGb3 | .740 | |||||
PB2 | .739 | |||||
ENGb1 | .722 | |||||
ENGe1 | .665 | |||||
EE2 | .859 | |||||
PTR2 | .843 | |||||
PTR1 | .815 | |||||
EE4 | .772 | |||||
PTR3 | .672 | |||||
EE3 | .600 | |||||
EE1 | .416 | .493 | ||||
PEU2 | .963 | |||||
PEU1 | .913 | |||||
PEU4 | .819 | |||||
PEU3 | .552 | |||||
SAT2 | .814 | |||||
SAT1 | .783 | |||||
SAT3 | .586 | |||||
ENGc2 | .875 | |||||
ENGc1 | .757 | |||||
ENGc3 | .638 | |||||
PU2 | .837 | |||||
PU1 | .828 | |||||
PU3 | .560 | |||||
PU4 | .530 | |||||
Eigenvalue | 12.285 | 3.797 | 1.792 | 2.968 | .959 | .828 |
Percent of variance | 39.660 | 12.249 | 5.781 | 9.573 | 3.095 | 2.670 |
Cumulative percent of variance | 39.660 | 51.909 | 57.690 | 67.263 | 70.358 | 73.028 |
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1. Health self-engagement: The first dimension (which explains 39.6% of the variance of the phenomenon) can be briefly defined as health self-engagement, as it consists of items relating to aspects concerning self-care in terms of benefits perceived by the use of the telemonitoring platform. Any improvement in the state of health passes first through the emotional and behavioural engagement of the patient, pushing him to provide for his psycho-physical well-being through the support provided by digital technology. This is also consistent with the research conducted up on patient engagement in the healthcare sector [57] which identifies the emotional component, and especially empathy during the interaction in a patient social network system, as an important factor affecting patient engagement and consequently the intention to use the platform [58]. Therefore, the emotional and behavioral dimensions, as well as the perceived benefits contribute to rendering effective the management of one's own health. Health self-engagement is an unprecedented factor in the panorama of the factors that are activated on the digital health platforms and is the main contribution of this paper.
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2. Technological risk.The second dimension (which explains 12.24% of the variance of the phenomenon) can be briefly defined as the technological risk, as it is composed of constructs related to the risk and difficulty perceived by the patient in using a new technological tool. Factors related to privacy, security or changing one's habits are aspects that can interfere with the use of the healthcare platform and therefore discourage its adoption. The combination of technological risk construct and effort expectancy construct in the same factor is consistent with the literature on e-service research [29, 59]. Indeed, Featherman and Pavlou [29] integrate perceived risk into the e-services adoption model and conclude that e-services adoption is adversely affected by performance-based risk perceptions and the customers' effort to adopt the e-service platform. Perception of technological risk prevents patients from using digital health platforms. For this reason, according to studies investigating the facilitated conditions to use of digital platforms in the healthcare industry [28], individual's control belief regarding the availability of resources and support structures to facilitate system use can affect the technology acceptance and the use of e-service positively [60, 61]. As a result, reduced cognitive effort and perceived risk levels should add to the IT system's instrumental benefits, such as increased performance [59].
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3. Perceived ease of use. This dimension (which explains 5.78% of the variance of the phenomenon) is a determining aspect for a prolonged participation of the patient in telemonitoring activities. Patients initiate active and collaborative participation mechanisms only when the use of the platform is simple and understandable [36]. A perception of simplicity can guarantee prolonged use of the platform thus becoming a good substitute for the traditional health service. This factor constitutes an interesting indicator for the process of streamlining the work of doctors and customizing the service to the real needs of the patient.
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4. Patient satisfaction: this dimension (which explains 9.57% of the variance of the phenomenon) measures the patient's response after the experience with the healthcare platform. It defines the patient's overall assessment and general impression. This aspect is essential for verifying the effectiveness of a "Connected Care" patient-centred approach with shared and integrated digital health models. According to the previous research [62, 63], to guarantee the best result, these models must be based on “relationship-centred care” that exploits patient engagement to guarantee a satisfactory result of the health service, especially in periods of overload of the health system. Previous studies demonstrated that satisfaction is influenced by the performance of e-services involving innovative and technological complex [64]. Moreover, recent literature demonstrates that satisfaction has a crucial role in e-services loyalty [65]. Therefore, because satisfaction is another critical factor activated into the digital platforms, it is crucial to have systems that guarantee continuous engagement contributing to patient and e-health service provider satisfaction with their relationships [62, 65].
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5. Patient Cognitive Engagement: (explains 3.09% of the variance of the phenomenon) compared to what is stated in the literature on patient engagement which sees it as a three-dimensional construct (cognitive, emotional and behavioural) [6, 66], our research demonstrates that during the patient's interaction with the COVID-19 telemonitoring platform, cognitive engagement is a determining dimension in its own right that encourages the patient's cognitive engagement while using the platform. The results of this research highlighted that the patient's cognitive sphere is activated and engaged regardless of the emotional and behavioural dimension.
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6. Perceived usefulness. This sixth dimension (which explains 2.67% of the variability of the phenomenon) includes all those aspects that justify the use, mostly prolonged (for 42% for more than a month, see Table 4), of the digital platform by patients. To ensure its prolonged use, the telemonitoring service must be perceived as effective and useful both for following the evolution of the infection and guaranteeing quick and easy feedback on the health status. This consideration is consistent with preceding research that demonstrates that telemonitoring services' effectiveness depends on perceived usefulness [67]. In particular, when a health digital platform does not guarantee feedback and patient involvement, it is not perceived as valuable and beneficial for patients [67]. On the other hand, both for patients and physicians, perceived usefulness positively affects the intention to use e-health services [59, 63, 67].
Effectiveness of the Telemonitoring platform for COVID-19
Factors | Sum of Squares | df | Mean Square | F | Sig | |
---|---|---|---|---|---|---|
Self-Health engagement | Between Groups | 1.379 | 2 | .690 | .308 | .736 |
Within Groups | 260.089 | 116 | 2.242 | |||
Total | 261.468 | 118 | ||||
Technological risk | Between Groups | 4.196 | 2 | 2.098 | 2.074 | .130 |
Within Groups | 117.360 | 116 | 1.012 | |||
Total | 121.556 | 118 | ||||
Perceived ease of use | Between Groups | 4.598 | 2 | 2.299 | 1.726 | .183 |
Within Groups | 154.513 | 116 | 1.332 | |||
Total | 159.112 | 118 | ||||
Satisfaction | Between Groups | 6.940 | 2 | 3.470 | 2.654 | .075* |
Within Groups | 151.658 | 116 | 1.307 | |||
Total | 158.598 | 118 | ||||
Cognitive engagement | Between Groups | 1.012 | 2 | .506 | .400 | .671 |
Within Groups | 146.527 | 116 | 1.263 | |||
Total | 147.538 | 118 | ||||
Perceived usefulness | Between Groups | 4.016 | 2 | 2.008 | 1.184 | .310 |
Within Groups | 196.772 | 116 | 1.696 | |||
Total | 200.788 | 118 |
Patient-target | ||||||||
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Value | No COVID-19 | Yes COVID-19 | Perhaps COVID-19 | Total | ||||
N | % | N | % | N | % | N | % | |
< 3 | 5 | 50 | 5 | 50 | 0 | 0 | 10 | 100 |
> 3 ≤ 5 | 11 | 31.4 | 14 | 40.0 | 10 | 28.6 | 35 | 100 |
> 5 | 24 | 32.4 | 45 | 60.8 | 5 | 6.8 | 74 | 100 |
Total | 40 | 33.6 | 64 | 53.8 | 15 | 12.6 | 119 | 100 |