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Promoting self-management and independent living of elders with chronic diseases through technology: A study of self-reported needs, priorities, and preferences.

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19 June 2023

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Abstract
Background and Objectives: Elders’ needs are rarely examined beforehand, and thus, although technology-based tools can enhance self-management, acceptability rates are still low. This study aimed to examine and compare self-reported needs, priorities, and preferences of elders with heart failure (HF), diabetes mellitus (DM), and chronic obstructive pulmonary disease (COPD) toward technology use to enhance self-management. Materials and Methods: A convenience sample of 473 participants over 60s (60.5% females), diagnosed with HF (N=156), DM (N=164), or COPD (N=153) was recruited. They were administered a questionnaire about the usefulness of technology in general and in specific areas of disease management. Results: Most participants (84.7%) admitted that technology is needed for better disease management. This was equally recognized across the three groups both for the overall and specific areas of disease management (in order of priority: ‘Information’, ‘Communication with Physicians & Caregivers’, and ‘Quality of Life & Wellbeing’). Sociodemographic differences were found. Cell phones and PCs were the devices of preference. The four common features prioritized by all three groups were related to ‘information about disease management’ (i.e., monitoring symptoms, reminders for medication intake, management and prevention of complications), whereas the fifth one was related to ‘communication with physicians and caregivers (i.e., in case of abnormal or critical signs). The top disease-specific feature was also monitoring systems (of respiratory rate or blood sugar or blood pressure, oxygen), whereas other disease-specific features followed (i.e., maintaining normal weight for HF patients, adjusting insulin dose for DM patients, and training on breathing exercises for COPD patients). Conclusions: Elders in these three samples seem receptive to technology in disease management. mHealth tools, incorporating both common and disease-specific features and addressing different chronic patients, and being personalized at the same time, could be cost-saving and useful adjuncts in routine clinical care to improve self-management.
Keywords: 
Subject: Public Health and Healthcare  -   Other

1. Introduction

Health prevention and promotion of the elderly are the most important public health challenges that Western societies will have to face in the coming years. Alongside the growing life expectancy, the incidence and prevalence of chronic non-communicable diseases (CNCD; e.g., cardiovascular diseases, chronic respiratory diseases, and diabetes mellitus) are reaching an epidemic level with 71% of deaths each year [1]. The increasing numbers of CNCDs compromise people's productivity and quality of life, result in steadily increasing healthcare utilization and, therefore, excessive national healthcare expenditures [2].
Self-management (e.g., disease monitoring, medication intake, decision-making, lifestyle modifications) [3] enhances patients’ autonomy and sense of control over the disease and is considered one of the most important factors in ensuring health prevention and promotion. It is associated with adherence to therapy, reduction in hospitalizations, decreased healthcare costs, improved health outcomes, and quality of life [1,4]. Yet, actively engaging chronic patients in self-management has been difficult [5]. Digital health technologies (DHT), such as web-based solutions, eHealth, and mHealth (i.e., mobile apps), are promising, patient-centered, and cost-effective tools that offer the possibility of self-management of CNCDs (e.g., improve disease monitoring, increase adherence, promote healthy lifestyles, and improve health-related behaviors) [6]. DHT has been associated with improved quality, accessibility, cost, and efficiency of healthcare, such as reductions in mortality rates, hospitalizations, and readmissions [7], along with substantial improvements in independent living, adherence, and quality of life [8].
In spite of the fact that the unprecedented speed of proliferation and availability of DHT has made them abound nowadays, and relevant studies have suggested DHT be a useful facilitator in the prevention or management of CNCDs, particularly in the elderly [9], acceptance and adoption rates remain relatively low among the elderly [10]. The studies on the effectiveness of DHT in self-management have been limited [11] and, though promising [12] have not yet provided convincing evidence, and results are rather mixed and inconsistent [13,14,15]. Old age is intertwined with features such as low health literacy, and limited smartphone experience [16,17,18], and older people typically have low adherence, low eHealth use [13,19], and low commitment levels. Though reluctantly curious, they have also been shown to be ambivalent towards e-health [20].
Understanding and accounting for older people’s needs and preferences in the development of a mHealth tool prior to its design could potentially enhance DHT benefits by increasing acceptance, commitment, and clinical outcomes [21,22]. However, research on patients’ needs has been lacking and it typically focuses either on specific diseases or on other age groups than the elderly [23]. Studies mainly examine features and effectiveness (feasibility and usability, and health outcomes) of mHealth tools [24,25,26] once they have been designed and used [15] and barriers and facilitators in using apps (intention, acceptance), such as the characteristics of a device (e.g., user-friendly interface, intriguing features) [10,27], and very few have examined elderly patients’ attitudes, beliefs, experiences, and expectations towards e-health services as factors influencing commitment to such tools. [20]. Elders’ self-reported needs are rarely addressed or prioritized in the design or implementation phases of DHT and few applications have been evidence-based such as need analysis studies [28].
Therefore, the purpose of this study was to examine self-reported needs, priorities, and preferences of elders suffering from chronic diseases toward technology use to enhance autonomy in disease management, and also explore potential differences between three chronic diseases (i.e., heart failure, diabetes mellitus, and chronic obstructive pulmonary disease).

2. Materials and Methods

This study was conducted within the framework of the project entitled ‘Empowered: An Integrated, intelligent assistant to support independent living for the elderly’, funded by the action RESEARCH-CREATE-INNOVATE Operational Programme Competitiveness, Entrepreneurship, and Innovation (ESPA 2014-2020) (EPAnEK) (Ref. No 5070922/29-9-2020).

2.1. Study Design and Participants

This is a cross-sectional study, conducted from May until July 2022. Eligibility criteria were people (1) primarily diagnosed with at least one of the following diseases: heart failure (HF), diabetes mellitus (DM), or chronic obstructive pulmonary disease (COPD) and (2) aged over 60 years old. A sample of 473 people diagnosed with either heart failure (N=156, 33.0%) diabetes mellitus (N=164, 34.7%), or chronic obstructive pulmonary disease (N=153, 32.3%) participated in the study. They were mostly females (60.5%), 71-80 years old (33.4%), single or divorced (52.9%), unemployed (59.4%), and had elementary education (35.1%). The detailed socio-demographics of the three samples can be found in Table 1.

2.2. Instrument

A questionnaire was developed by the researchers, which included two sections that were common among the three groups of patients: 10 socio-demographic questions and 21 questions, allocated in three subgroups, about patients' views on the extent that the use of technology could be useful in various aspects of disease management: quality of life/well-being (8 questions, such as dealing with stress and depression, reminders for health-enhancing behaviors, such as exercise, diet, medical appointments, positive reinforcement for achieving disease improvement goals); information & disease management (7 questions, such as monitoring of current health status, reminders to take medication, prevention, and management of disease-related complications); communication with physicians and caregivers (6 questions, such as warning and caregiver notification when necessary or immediate medical attention is required). Additionally, a third section was selectively administered to the patients, depending on their disease, to record their views on whether technology use can help various aspects of the disease-specific management: heart failure patients (4 questions; e.g., maintain a normal weight, monitor respiratory rate), diabetes mellitus patients (7 questions; e.g., monitoring of vision, blood sugar, blood pressure, cholesterol levels, adjustment of insulin dose), and chronic obstructive pulmonary disease patients (10 questions; e.g., increase fluid intake, coping with shortness of breath, use of monitoring devices for blood pressure, oxygen, etc). A five-point Likert-type scale, ranging from ‘not at all’ to ‘very much’, was used to rate responses.

2.3. Procedure

A convenience sampling method was used for data collection since researchers had access to potential participants. The questionnaire was administered through interviews or individually using a paper-and-pencil form of the questionnaire. The mean time to complete the questionnaires was approximately 15 min. There was no compensation for participating in the study. Ethics and confidentiality were assured throughout the study process. The study was conducted in accordance with the Declaration of Helsinki and approved by the Hellenic Mediterranean University Ref. No. of approval 74/θ.21/18.11/2020).

2.4. Statistical Analysis

Statistical analysis was conducted using IBM SPSS Statistics for Windows, v.26.0, Armonk, NY: IBM Corp.). Self-management of the disease was assessed with three questions: the perceived ability of self-management, the need for support on disease management, and the need for technology to better disease management. The usefulness of technology was assessed in three areas of disease management: Quality of Life/Well-being (8 items), Information & Disease Management (78 items), and Communication with Physicians and Caregivers (6 items). For the comparison of frequency distributions, the 95% confidence intervals (95% CI) were calculated respectively, while the differences in response distributions in the aspects of disease management (i.e., Quality of Life & Well-Being, Information & Management of the Disease, and Communication with Physicians and Caregivers) were estimated through the method Friedman. Their validity and reliability were determined through independent cross-questioning. The shape of the distributions of the scores of the three aspects of disease management was checked through the Blom method (QQ plot) and due to slight asymmetry, their univariate correlation followed with the Pearson parametric method both among themselves and with the characteristics of the participants. This was followed by an analysis of variance test between the three aspects of disease management and Student t regarding the categories of the characteristics. An acceptable level of significance was set at 0.05.

3. Results

3.1. Self- or supported management of disease

In Table 1 can be seen that, although the vast majority of the participants (nearly 90%) reported that they moderately (42.9%) or very much (46.3%) could manage their disease satisfactorily, the majority (nearly 65%) also reported that they need moderately (30.2%) or very much (34.5%) support to this. Nearly 85% believed that the use of technology (e.g., a smartphone application) can better help disease management, either moderately (30.2%) or very much (54.5%).

3.1.1. Usefulness of technology in disease management

The participants reported a significantly higher need for technology use in the area of Information & Disease Management (M=3.57) and Communication with Physicians and Caregivers (M=3.51) compared to Quality of Life & Wellbeing (M=3.38) (p<.001). The detailed responses, hierarchically, in each of the three areas of disease management can be found in Table 2.

3.1.2. Usefulness of technology and sociodemographic characteristics

Those with higher (i.e., postgraduate) education (3.90±0.49), those employed (3.57±0.51), and those with a very good financial situation (3.42±0.45) reported significantly higher scores in the usefulness of technology in Information & Disease Management, Communication with Physicians and Caregivers, and Quality of Life & Well-being respectively, compared to those with lower education (Elementary, 3.47±0.53; p<0.05), not being employed (3.46±0.54; p<0.05), and with a bad financial situation (3.25±0.50; p<0.05) (results not shown in table/figure).

3.2. Usefulness of Technology in Disease Management across the three groups of patients

High mean scores on the usefulness of Technology in disease management (overall and its three aspects) were found for the three groups of patients (scores ranging from 3.4 to 3.6). However, no significant differences were found between the three groups of patients neither in the overall disease management score nor in its three aspects (results not shown in table/figure).

3.2.1. Device Preference for disease management

The rates of patients reporting the device of their preference for helping them in disease management can be seen in Table 3. Cell phones and PCs outweighed the rest of the devices. DM and COPD patients prioritized the cell phone over the rest of the devices and secondly, the PC, whereas the rates for HF patients are vice versa.

3.2.2. Preference of disease-specific features for disease management

In Table 4 can be seen that the HF patients reported the highest mean score on ‘Record/monitor of respiratory rate and single-lead electrocardiogram’ (3.60±1.09) and the lowest on ‘Appropriate exercise program’ (3.46±0.87) (p>0.05). The DM patients reported the highest mean scores on ‘Measuring/monitoring blood sugar’ (3.84±1.00) and the lowest on ‘Monitoring/control of weight’ (3.46±1.13) (p=0.004). The COPD patients reported the highest mean scores on ‘Use of monitoring devices/equipment’ (3.73±0.97) and the lowest on ‘Increased fluid intake’ (3.39±1.01) (p>0.05).

4. Discussion

This study aimed at exploring the needs, priorities, and preferences of three groups of elders suffering from chronic diseases regarding the usefulness of technology in enhancing disease management in three areas (quality of life/well-being, information, and communication with physicians and caregivers).
Although the vast majority of the participants reported that they could manage their disease satisfactorily in general, the majority also reported that they need support for this. Consistent with others [29], these findings show that patients have unmet needs in several domains of disease management. These unmet needs could comprise the targets of relevant interventions and application development. Pertinent to this, is the equally reported belief in the usefulness of technology (e.g., a smartphone application) in better disease management by the three sampling groups. Taking into consideration that the use of technology-based applications among users of any age in healthcare is very low [30], and old people have even lower rates [9], this is a noteworthy and also reassuring finding which suggests that old people in these three samples are receptive to the use of technology in the management of their disease.
The examination of demographics showed that those well-educated prioritized the need for technology use in the area of information, those employed prioritized the need for technology use in the area of communication with physicians and caregivers, and those with very good financial situations, in the area of quality of life & well-being. The findings were in line with others that have shown that occupational status, high income, and education are associated with high eHealth use [13,19]. Well-educated people seem to value knowledge and information to facilitate disease management. People with high income seem to have the resources that allow them to ensure information and access to healthcare thus well-being is a priority. Reasons for preference of the area of communication with physicians and caregivers by those employed are less clear; however, it could be due to time constraints.

4.1. Common and Specific features

Respondents in all three groups of patients equally identified that technology could be useful in overall disease management and in its three aspects. Rather quite plausibly, Quality of Life & well-being followed the other two first-choice areas of disease management. Participants prioritized technology use in providing Information and secondly communication with physicians and caregivers.
The areas of disease management that participants identified as struggling most and needing help from technology were both a group of common and a group of disease-specific features. As far as the common features are concerned, it is noteworthy that four features related to information for disease management received the highest scores and outweighed the others. such as the use of monitoring devices/equipment (e.g., blood pressure, oxygen, glucose, etc.), reminders for medication intake, management of disease-related complications (e.g., diabetic foot care, infections for COPD, etc.), and prevention of disease-related complications (e.g., foot control, infections for COPD, etc.). Not surprisingly, the need for technology-based options for the three CNCDs prioritized monitoring systems [14,31,32]. Prevention and management of complications through mHealth, such as diabetic foot, have received a lot of attention [33], and medication adherence is one of the top priorities in health apps [10,34,35]). The fifth feature came from the communication with physicians and caregivers’ area (i.e., notifying the caregiver when there are abnormal or critical signs). To the authors’ knowledge, the involvement of the caregiver has been predominately examined in educational interventions of increasing their familiarity with technology [35], whereas Slevin found that COPD patients believed that data derived from DHT could potentially facilitate their interaction with physicians [5]. Fears and insecurity about an unexpected health problem or complication could also explain this finding.
The device of preference is cell phones for DM and COPD patients, and PC for HF patients. We cannot know why this difference was found.
In terms of the disease-specific features, the findings were as anticipated [14,31,32,35]: Specific monitoring systems were the first choice by all three groups of patients (recording/monitoring respiratory rate and single-lead electrocardiogram for HF patients [31,35], measuring/monitoring blood sugar for DM [32], and monitoring devices/equipment for blood pressure, oxygen, glucose, etc. for COPD patients [14]), whereas other disease-specific features followed: maintaining normal weight for HF patients, adjusting insulin dose according to glucose levels for DM patients, and training on breathing exercises for COPD patients. It can be assumed that elders have limited access to healthcare resources and thus, their need for remote monitoring is increased. For this, a vast number of monitoring devices have been developed for these diseases [14,22,28,36].

4.2. Limitations

This study has a number of limitations. We don’t know whether and to what extent participants had access to healthcare, help from informal caregivers, prior experience and skills of technology use, what were their perceptions and attitudes towards health apps or technology in general, all of which may have impacted participants’ attitudes about the importance and usefulness of technology in disease management.
Despite the limitations, one strong point of this study was that it explored patients’ needs before developing any mHealth tool, which could guide individualized and tailored solutions. A second one is that the study examined multiple chronic diseases, rather than one, which adds to the understanding of the patients’ common and unique needs [25]. Given the satisfactory number of participants in each group of patients, comparisons between the groups were also allowed. The findings of this study may inform the design of apps to simultaneously address multiple chronic patients and be individualized according to their needs and preferences [37].

5. Conclusions

The findings of this study showed that technology-based tools, such as mobile apps, are needed by elders with CNCDs to enhance self-management, improve their independence, and help them achieve optimal health outcomes. Findings also suggest that both common and disease-specific features could be incorporated into mobile apps to improve self-reported management deficits. Mobile apps can be an easy and accessible way to track symptoms and medication adherence and also connect with healthcare providers and caregivers when needed.
Technology and mHealth interventions in general can serve as effective alternatives that increase self-management, and as long as they are accepted by the users, may be useful adjuncts in routine clinical care. They could be incorporated into an integrated management plan by physicians, nurses, and allied health practitioners to increase self-management and enrich care both within and outside the clinical setting. Technologically based self-management solutions can be cost-effective and alleviate significant burdens on professionals, caregivers, and healthcare services in the long run. However, in order to be effective, apps should be evidence-based [10] and align with patients’ personalized self-management goals and plans. These features may increase patients’ engagement in self-management [21,22].

Author Contributions

Conceptualization, M.R., A.S., and A.K.; methodology, M.R., A.S., and A.K.; software, Al.D., An.D. and D.K.; validation, G.V., A.P. and G.T.; formal analysis, M.L.; investigation, S.K., G.V., E.K.; resources, N.R., S.K., M.R.; data curation, M.L.; writing—original draft preparation, A.K.; writing—review and editing, M.R., A.S.; visualization, Al.D.; supervision, M.R.; project administration, M.R.; funding acquisition, M.R., Al.D., A.S., and A.K. All authors have read and agreed to the published version of the manuscript.

Funding

This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T2EDK-00586).

Institutional Review Board Statement

The study was conducted in accordance with the Declara tion of Helsinki, and approved by the Hellenic Mediterranean University Ref. No. of approval 74/θ.21/18.11/2020).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request to the corresponding authors.

Acknowledgments

Sincere gratitude should be expressed to all participants in this research study.

Conflicts of Interest

The authors declare no conflict of interest.

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Table 1. Sociodemographic characteristics of the three samples.
Table 1. Sociodemographic characteristics of the three samples.
Total sample
(N=473)		 Heart failure (N=156) Diabetes mellitus (N=164) Chronic obstructive pulmonary disease (N=153)
n % n % n % n %
Gender Male / Female 187 / 286 39.5 / 60.5 63/93 40.4/59.6 67/97 40.9/59.1 57/96 37.3/62.7
Age (years) 60-70 147 31.1 56 35.9 51 31.1 40 26.1
71-80 158 33.4 50 32.1 57 34.8 51 33.3
81-90 133 28.1 43 27.5 44 26.8 46 30.1
>90 35 7.4 7 4.5 12 7.3 16 10.5
Family status Married, Cohabited 223 47.1 83 53.2 74 45.1 66 43.1
Single, Divorced 250 52.9 73 46.8 90 54.9 87 56.9
Education Up to Elementary 166 35.1 54 34.6 56 34.1 56 36.6
Junior high school 120 25.4 31 19.9 43 26.2 46 30.1
High school 83 17.5 31 19.9 31 18.9 21 13.7
University / College 82 17.3 32 20.5 28 17.1 22 14.4
Postgraduate studies 22 4.7 8 5.1 6 3.7 8 5.2
Financial situation Bad 40 8.5 9 5.8 18 11.0 13 8.5
Moderate 224 47.4 74 47.4 68 41.5 82 53.6
Good/Very Good 209 44.2 73 46.8 78 47.6 58 37.9
Employment (yes) 192 40.6 66 42.3 67 40.9 59 38.6
Disease Heart failure 156 33.0 144 30.4
Diabetes mellitus 164 34.7 22 4.7 145 30.7
Chronic obstructive pulmonary disease 153 32.3 10 2.1 26 5.5 126 26.6
Perceived self-ability / Self-management Not at all 51 10.8 18 11.6 20 12.2 13 8.5
Moderately 203 42.9 62 39.7 67 40.8 74 48.4
Very much 219 46.3 76 48.7 77 47.0 66 43.1
Need for support Not at all 167 35.3 50 32.1 58 35.4 59 38.6
Moderately 143 30.2 49 31.4 51 31.1 43 28.1
Very much 163 34.5 57 36.5 55 33.5 51 33.3
Need for technology Not at all 74 15.6 22 14.1 23 14.0 29 19.0
Moderately 142 30.0 51 32.7 55 33.5 36 23.5
Very much 257 54.4 83 53.2 86 52.5 88 57.5
Table 2. Hierarchical distribution of responses to the usefulness of technology in the three areas of self-management (i.e., quality of life/well-being, information & disease management, and communication with physicians and caregivers).
Table 2. Hierarchical distribution of responses to the usefulness of technology in the three areas of self-management (i.e., quality of life/well-being, information & disease management, and communication with physicians and caregivers).
M SD
Quality of Life/Well-being 3.38 0.46
1. Reminders for health-enhancing behaviors (e.g., exercise, diet, medical appointments, etc.) 3.54 1.02
2. Quality of life improvement 3.53 0.95
3. Positive reinforcement for achieving disease improvement goals 3.53 1.04
4. Record of current health status 3.49 0.94
5. Maintaining optimism and hope 3.42 1.03
6. Quality of sleep improvement 3.30 0.92
7. Dealing with stress 3.05 1.15
8. Dealing with depression 2.99 1.08
Information & Disease Management 3.57 0.50
9. Use of monitoring devices/equipment (e.g., blood pressure, oxygen, glucose, etc.) 3.74 1.00
10. Reminder on medication intake according to medical instructions 3.66 0.94
11. Management of disease-related complications (e.g., diabetic foot care, infections for COPD, etc.) 3.66 1.09
12. Prevention of disease-related complications (e.g., foot control, infections for COPD, etc.) 3.64 1.02
13. Increasing knowledge about disease 3.57 1.08
14. Education - Adoption of proper nutrition 3.51 1.07
15. Stop smoking 3.02 1.33
Communication with Physicians and Caregivers 3.51 0.54
16. Notifying the caregiver (family or doctor) when there are abnormal or critical signs 3.60 1.01
17. Warning when immediate medical assistance is required 3.51 1.04
18. Direct contact with the health care system when required 3.50 1.07
19. Increase in perceived support (e.g., an app helps you manage your illness) 3.49 0.99
20. Improving communication with health professionals 3.48 1.01
21. Scheduling visits to the doctor 3.46 1.01
Table 3. Device preference for disease self-management.
Table 3. Device preference for disease self-management.
HF (N=156) DM (N=164) COPD (N=153)
Cell phone 26.3 27.4 28.8
PC 27.6 23.2 20.9
Tablet 23.1 22.0 17.6
Smartwatch 12.8 13.4 17.6
Other 10.3 14.0 15.0
Note. Numbers are percentages. HF=Heart failure patients; DM=Diabetes mellitus patients; COPD= Chronic obstructive pulmonary disease patients.
Table 4. Hierarchical distribution of responses (means and standard deviations) for the preference of disease-specific features for disease management.
Table 4. Hierarchical distribution of responses (means and standard deviations) for the preference of disease-specific features for disease management.
M (SD)
Heart failure patients (HF) 3.53 (0.60)
1. Record/monitor respiratory rate and single-lead electrocardiogram 3.60 (1.09)
2. Maintaining a normal weight 3.54 (1.03)
3. Appropriate exercise program 3.46 (0.87)
Diabetes mellitus patients (DM) 3.60 (0.54)
1. Measuring/monitoring blood sugar 3.84 (1.00)
2. Adjusting insulin dose according to glucose levels 3.73 (1.02)
3. Measuring/monitoring cholesterol 3.59 (1.06)
4. Eye check 3.54 (1.08)
5. monitoring blood pressure 3.46 (1.13)
6. Monitoring/control of weight 3.46 (0.85)
Chronic obstructive pulmonary disease patients (COPD) 3.53 (0.45)
1. Use of monitoring devices/equipment (eg for blood pressure, oxygen, glucose, etc.) 3.73 (0.97)
2. Training on breathing exercises 3.63 (1.03)
3. Knowledge of the use of an oxygen supply device 3.60 (1.03)
4. Appropriate exercise program 3.56 (0.94)
5. Treating shortness of breath 3.50 (1.06)
6. Weight maintenance 3.50 (1.03)
7. Maintaining optimism and hope 3.44 (1.03)
8. Dealing with the fear of possible shortness of breath 3.42 (1.24)
9. Increased fluid intake 3.39 (1.01)
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