Technology engagement in enhancing memory functionality in elderly care centers

Abstract

BACKGROUND:

Studies on technology engagement among the elderly have shown that cognitive function is associated with technology engagement. However, the impact of technology engagement on elderly memory functionality has yet to be determined. The current study investigates the impact of technology engagement on memory functionality among elderly care centers in Jordan.

MATERIALS AND METHOD:

A descriptive longitudinal study of population consisted of 470 residents at the selected care centers aged 65 years and above from Amman, Jordan in 2024. The purposive sampling method was used in sampling the participants. A reciprocal 2-year longitudinal study of technology engagement and memory functionality. The data was collected quantitatively using “Everyday Technology Use, Episodic Memory, Executive function, Clock Drawing Test (CDT), and Cognitive Assessment” tools and qualitatively using semi-structured interviews. The data analyzed descriptively and inferentially using NVivo and SPSS 23.0.

RESULTS:

The analyzes showed that technology engagement had longitudinal positive effects on cognitive function over 2 years. The results showed that reciprocally, greater use of technology was significantly associated with better memory performance in the following years, B (SE) =0.20, SE =0.02, P < .001. The lagged effect of technology use on executive function was significant across waves, B = .27, SE =.015, P < .001. However, the lagged effect of executive function on the use of technology was not significant, B = .00, SE =.01, P = .14.

CONCLUSION:

The study contributed to a clearer understanding of the impact of technology engagement on memory function.

Introduction

Memory processes become crucial for the comprehension of how these technological innovations happen, and this is essential for predicting the future memory care scenario in a society like Jordan. The global demographic landscape is currently undergoing a new phase of dramatic transformation manifested by the fastest-growing elderly population.[1] The aging population entails a close-up examination of the sophisticated dance among the technologies, social interaction, and cognitive health of the elderly.[2] In the context of Jordan where cultural values have a great role in people’s way of thinking and economic growth, this research sets its aim at looking into the intricacies of the relationship between computer use, social media engagement, and the shape of memory and recall.[3] Appreciating the complexity of population aging and intricate political contexts shaping it enables us to formulate the influence of computer use and memory in social media as a venial research problem.[4] The primary purpose is to identify the technology’s effect on the human cognition process leading to the improvement in the well-being of the elderly in total care homes. While Talamo et al.,[5] focus on the key issue of digital literacy through social networks, by linking these competencies to the relevance and usefulness of recall function in old people’s home centers, the article also plunges deeper into the profound meaning of sustaining social relationships as people age, both physically and psychologically. The methodology used in the research further builds on the base to study the involvement of technologies, which can be used in the creation of person-centered and meaningful activities for people who are experiencing cognitive changes, especially dementia patients.[6,7] With recent advancements in technology, smart voice assistants have appeared on the scene, providing junior users with many interesting options, not least, very efficient ways of interacting with digital devices.[8,9] These are not only the technologies that take care of the tasks in practicality but also are useful in manipulating the cognitive experience of seniors.[10] The scale of the dialogue intensifies with deeper subjects like recent developments in memory devices and the usage of in-memory computers.[11,12] The primary aim of this study is to provide a profound understanding of how technology and social media alter the formation and retrieval of memory in the elderly community, specifically with a focus on those residing in care centers. In Jordan, the use of technology among the elderly has noticeably increased, and debates on the effect of technology on the elderly hurt their health. However, no study has yet been conducted in Jordan to investigate the effect, especially on their memory retrieval.

Zaine[13] emphasizes the technical aspect of the construction and an evaluation of multimedia packages with the purpose of social interaction among the elderly. Hence, this study approaches the social part of technology use and gives an opportunity to learn about the influence on memory functions, along with what we are dealing with - memory function changes. Farsi[14] dealt with the literature survey of social media usage by healthcare providers which offers useful standings for the inclusion of healthcare facilities and the appraisal of possible implications of memory functioning in nursing homes. Bixter[15] applies and expands the idea of the UTAUT model, which is of high significance when we are talking about social communication technologies adoption among older adults and making the use of computers and social media a daily routine for the elderly. Pradhan et al.[8] have captured the experience of intelligent voice assistants among socio-technological backward old adults having a low level of technology use. This information helps prospective efforts for memory-based knowledge enhancements and rethink the voice assistant roles of the elderly. Sebastian et al.[16] aimed at in-memory computing devices and applications, following trends in technologies that could later be used for memory-related treatment, but not limited to. Henry[17] looks into the prospective memory impairments resulting from brain ailments, serving as the basis for comprehending the memory problems of the senior citizens particularly the ones in the nursing and long-term care homes. It is demonstrated by Madore et al.[18] that occurrences of attention lapses lead to future memory failures and add to our knowledge about the effects of multitasking in the media on memory. Importantly, this particular aspect is currently under review in regard to computer use and social network engagement of elderly people.[19]

Albert Bandura’s social cognitive theory

Albert Bandura’s Social Cognitive Theory underscores the reciprocal influence of individual factors, behavioral patterns, and environmental factors in shaping human behavior.[20] This theory posits that observational learning and social modeling are pivotal in determining behavior.[21] In the context of the elderly population engaging with technology and social media, individuals learn by observing the actions and experiences of those around them. The subsequent interactions guide the formation of attitudes, beliefs, and behaviors.[20] In the context of this study, Albert Bandura’s Social Cognitive Theory serves as the theoretical foundation for analyzing the cognitive skills of elderly individuals related to computer and social media use. The social cognitive theory puts forward the argument that scheming, induction, and social modeling are the core elements in explaining behavior.[22]

Aim of the study

This study explores the connection between internet use, social media interactions, and cognitive processes among elderly individuals in Jordanian care centers, emphasizing digital literacy evaluation and examining social media platform adoption.

Materials and Method

Study design and settings

Adopting a mixed-method research design, the study included both inferential and descriptive analysis, with three senior care facilities in Jordan providing the data used for the analysis.

Study participants and sampling

Respondents were Medicare beneficiaries living in Jordan-Amman’s chosen care facilities, who were 65 years of age or older. The comprehensive surveys about computer use, social media use, personal variables, health conditions, living environment, and socio-economic status were completed by 470 older adults (participation rate = 71%). Since the beginning of the investigation, interviews have been done one after the other. When a participant’s health or language limitations prevented them from answering the questionnaire completely, a proxy respondent was utilized. The information was gathered in February of 2024. Out of the initial sample, 550 people took part in the poll. Eighty deceased respondents were not included in this study because they were not given the questionnaires regarding computer use and memory function. Furthermore, because their questionnaire responses might have varied from those in the normal cognitive group, respondents who at baseline reported having been diagnosed with Alzheimer’s disease or dementia, either directly or through a proxy, were removed. Respondents with any observations on memory, computer involvement, and executive functioning made up the final analytic sample (470). Covariates were investigated in this study using logistic regression to explain the missing status; the outcome variable considered was whether the participants were lost owing to attrition; this was aimed at determining the cases of data missing at random (MAR). Hence, under the assumption of MAR, the full information maximum likelihood estimation was used to guard against possible biases in the estimated parameters resulting from selective attrition.

Data collection tools

Everyday technology use

According to Levine, Lipsitz, and Linder’s[23] prior study, “everyday technology use” refers to utilizing technology for routine work rather than medical needs. A set of yes/no questions (0 = no, 1 = yes) about the participants’ usage of (1) cell phones, (2) computers, (3) tablets, (4) text messages/emails, (5) internet for other purpose except email, (6) internet banking, (7) shopping online, and (8) social networking, were asked during the National Health and Aging Trends study. A composite score between 0 and 8 was generated by adding the yes/no responses to each question; higher scores denoted a wider range of commonly used technologies.

Digital health technology use. According to Levine[23] employing technology pertinent to one’s health care is known as digital health technology use. The participants were questioned about their usage of the Internet for the following purposes: (1) learning more about medical conditions; (2) contacting a physician; (3) filling or renewing treatment prescriptions; and (4) managing issues of health insurance. A binary scale was used to measure the responses (0 = no, 1 = yes). Higher total sum scores indicated more frequent usage of digital health technologies; the values ranged from 0 to 4.

Episodic memory

The participants were tested for immediate and delayed 10-word recall capability in a test for their episodic memory function.[24] The participants were presented with 10 terms from the “Health and Retirement Study”[25] and were required to memorize them instantly. After about 5 min, they are asked to recollect as many words as they can from the prior list of 10 words. The scoring was based on the number of properly recalled words which were summed to arrive at the immediate and delayed recall scores (within a range of 0–10).

Executive function

The “Clock Drawing Test (CDT)” was used to evaluate executive function. As per Dion et al.,[26] executive control functions are cognitive processes that coordinate actions and ideas into complex goal-oriented behaviors. The CDT is notorious for its capacity to assess these functions. On a piece of paper, the participants were told to completely illustrate a clock with its hands pointing in the direction of a certain time. The task could take up to two minutes for the participants to finish. Drawings were assessed using a standard criterion and a scale that went from 0 (not recognizable as a clock) to 5 (an exact representation of a clock).

Covariates

The following covariates were included: self-identified race, gender, education (<12 years, high school, college, bachelor’s degree, and postgraduate), and depression. These covariates were known to be associated with both technology use and cognitive function. The Patient Health Questionnaire 2 was used to measure depression by adding two items (PHQ-2).

Cognitive assessments

Adults within an age-dependent range were tested using cognitive tests that are designed and controlled to assess cognitive abilities (Papadopoulos, 2021). The evaluation examines a few cognitive domains, such as problem-solving, memory, and attention. Data from the pre-and post-tests are analyzed. Data relating to participants’ computer use and social media participation were collected using the tools adapted from.[15] The data was taken on the frequency, reasons, and duration of using computer devices and social media.

Validity and reliability of the tools

The developer of the scale calculated its reliability coefficient in two ways: The first method uses the Cronbach alpha coefficient that reached 0.674, with a high, statistically significant reliability coefficient. Half-splitting, the correlation coefficient reached 0.613, which indicates that the reliability is high. The researcher also calculated the validity of the scale through external validity, as the scale was presented to a group of 11 expert specialists in the fields of psychology and mental health. The data collection tools were reviewed by 5 expert professors in the field of psychology and technology. The feedback from the experts was considered. Six items from the test were removed as they were seen as not suitable for the setting of the study. In addition, 5 items were added based on the experts’ suggestions. The validity was calculated by calculating the correlation coefficients between each item and the degree of the dimension. All items are significant and therefore all are acceptable, which indicates the high internal validity of the measurement.

Qualitative measures

Some participants were interviewed to get qualitative data regarding their experiences with social media, computers, and memory effects using a semi-structured method, while the interview themes were defined using thematic analysis to identify and explore themes in the qualitative data. The thematic analysis approach used in this work was based on the imperatives from Braun and Clarke.[27] Themes on memory formation and retrieval were identified in the transcripts of interviews. Semi-structured interviews are employed in the process of gathering data. This study respects participant privacy and dignity by adhering to ethical guidelines. During the cognitive evaluations and interviews, informed consent was requested, and appropriate steps were taken to minimize any potential discomfort or upset. The analysis and reporting of data were confined, and confidentiality and anonymization were maintained. The study was ethically approved by the appropriate institutional review board.

Data analyzes

Initially, the sample characteristics and the relationships between the research variables were described using univariate statistics and bivariate correlations. Second, a number of autoregressive models (cross-lagged) were used to investigate the association between the usage of technology and cognitive abilities across time.[28] Compared to cross-sectional research, the model enables the parallel analysis of the two-way relationship between two variables of interest with more accuracy. This work examined two hypotheses: stationarity and measurement invariance across time points, before looking at the proposed model. According to Kim et al.,[29] the idea of measurement invariance is aimed at ensuring that the participants understand the items and respond consistently over time. A suitable autoregressive model must have weak factorial invariance.[28] For the latent variables, the factor loadings were made invariant across waves to verify this assumption.[30]

Ethical consideration

After the approval of the research project, an ethics code was obtained from the ethics committee of the university (IRB. UJ. 102-2023). All participants provided their informed written consent, and they were made aware of the research process and were assured of the confidentiality of their information. In addition, they were allowed to withdraw at any stage during the study.

Results

Overall, these findings provide an in-depth understanding of the complex relationship between technology use and memory-related outcomes in long-term care settings. The cognitive scores of the participants significantly improved, according to the cognitive examinations carried out as part of the intervention. Pre-intervention cognitive scores for a subset of ten subjects varied from 75 to 89, whereas post-intervention levels consistently increased from 78 to 92. This indicates a significant improvement in cognitive function for the entire sampled population.

In terms of social media and computer use patterns, participants exhibited diverse engagement frequencies and purposes. The table illustrates these variations, with participants engaging in computer use on a daily, weekly, or monthly basis, and social media engagement occurring daily, occasionally, or rarely. The purposes of computer use, and social media engagement included information seeking, entertainment, social connection, and information sharing, showcasing individual preferences and practices within the selected group.

Cognitive assessments

Cognitive evaluations of participants have shown profound increases in their cognitive scores with the implementation of the intervention. Table 1 shows the pre-and post-intervention scores on the cognitive measure for a subset of ten participants.

Table 1.

Cognitive assessment results

Results		Mean of pre-intervention cognitive score		Mean of post-intervention cognitive score
		80.09		85.5

Social media and computer use patterns

The social media and computer use surveys showed varied engagement among the participants. Table 2 highlights the computer use and social media engagement frequencies and purposes among the selected participants.

Table 2.

Social media and computer use patterns

Computer use frequency		Social media engagement frequency		Purpose of computer use		Purpose of social media use
Daily		Occasionally		Information Seeking		Social Connection
Weekly		Rarely		Entertainment		Information Sharing
Daily		Daily		Social Connection		Social Connection
Rarely		Never		Information Seeking		-
Monthly		Occasionally		Entertainment		Social Connection
Daily		Weekly		Information Seeking		Information Sharing
Weekly		Daily		Social Connection		Social Connection
Daily		Never		Entertainment		-
Weekly		Weekly		Information Seeking		Information Sharing
Monthly		Occasionally		Entertainment		Social Connection

Bivariate correlations among study variables

The analysis showed a stronger correlation between younger age and everyday technology use (r = −0.41, P < 0.001), higher education level (r = 0.51, P < 0.001), male gender (r = −0.12, P < 0.001), and lower depression level (r = −0.21, P < 0.001), according to the bivariate correlations among the research variables. The relationship between the adoption of digital health technologies and demographic factors showed similar trends. The usage of digital health technology and everyday technology use were found to be strongly correlated (r = .61, P < . 001) with all cognitive measures; higher levels of cognitive function were linked to greater use. Hence, there is a stronger correlation between cognitive function and everyday technology use compared to the level of correlation between cognitive function and digital health technology usage.

Assumption test

Initially, models of the latent constructs (i.e. episodic memory and technological engagement) were evaluated for their measurement invariance. The findings indicated that the premise of weak factorial invariance was upheld for the usage of technology (M2: χ² (3) =29.30, CFI = 0.989, P < 0.001, RMSEA = 0.051). Weak factorial invariance was also found for the memory function (M3: χ² (3) =6.95, CFI = 0.977, P > 0.05, RMSEA = 0.021). These findings suggest that, for memory function and TECHNOLOGY use, the minimally necessary assumption was met. Secondly, an assumption of stationarity was examined. The result displays some of the proposed covariate autoregressive cross-lagged models. Therefore, regarding the correlation between memory function and technology use, the model with equal cross-lagged and autoregressive effects across waves was chosen (M5: χ² (108) =977.21, P < 0.001, CFI = 0.982, RMSEA = 0.051). In the same way, the model that showed the same values for both cross-lagged and autoregressive path coefficients was chosen to explain the TECHNOLOGY use-executive function relationship (M5: χ² (65) =690.20, P < 0.001, CFI = 0.978, RMSEA = 0.052).

Final models

According to the study’s final models, word recall was a significant pre-technology or technology use in the future (B = .03, SE =0.015, P < . 001). Better memory performance in the years that followed was reciprocally substantially correlated with increased technology use, B (SE) =0.20, SE =.02, P < . 001. TECHNOLOGY use had a substantial lagged influence on executive function across waves (B = .27, SE =.015, P < . 001). However, there was no significant lag in the relationship between executive function and technology use (B = .00, SE =.01, P = . 14).

Sensitivity analyses

It is crucial to recognize that rather than representing the temporal relationships between technology use and cognitive performance, the reported findings may instead be the result of social selection effects. In other words, those who are generally more intelligent and in better cognitive health may be more inclined to utilize technology in the first place. Sensitivity studies were carried out to address this problem by dividing the sample into two groups according to educational attainment: those with more than a high school education (n = 1,984) and those with less than a high school education (n = 1,920). There were no differences between the high-functioning and low-functioning groups, and the results were similar to the analysis conducted with the total sample. We also incorporated self-ratings of health at each time point as a time-variant covariate in the models to account for the effect of health, and the outcomes held. These results imply that the temporal relationship between technology use and cognitive function that has been found is unaffected by an individual’s level of education or health.

Qualitative analyses

Thematic analysis of in-depth interviews provided insights into participants’ experiences with computer use and social media. Participants expressed a range of themes, including positive impacts on memory, challenges in learning technology, enhanced social connection, privacy concerns, information seeking, positive emotional impact, limited interest in technology, social connection and entertainment, learning opportunities, confidence in technology use, positive impact on daily activities, social media as a learning tool, entertainment and leisure, and mixed feelings about technology. These themes highlight the diverse impacts and challenges associated with technology adoption among the elderly, contributing rich qualitative data to the study. The main themes emerged from the interviews summarized in Table 3.

Table 3.

Resulted themes from the semi-structured interviews

Interview theme 1		Interview themes 2		Interview themes 3		Interview themes 4		Interview themes 5
Positive impact on memory		Challenges in learning technology		Enhanced cognitive engagement		Overcoming technological barriers		Technology and emotional well-being
Enhanced social connection		Privacy concerns		Technological empowerment		Navigating digital relationships		Personal growth through technology
Information seeking		Positive emotional impact		Exploring new technological horizons		Digital literacy journey		Technology and social connectivity
Limited interest in technology		-		-		-		-
Social connection and entertainment		Learning opportunities		Technology and well-being		Digital entertainment preferences		Digital learning experiences
Information sharing		Confidence in technology use		Social media as a learning tool		Positive impact on social life		Learning opportunities
Positive impact on daily activities		Social media as a learning tool		Digital literacy journey		-		-
Entertainment and leisure		-		-		-		-
Information seeking		Positive impact on social life		Technology and social connectivity		Exploring new technological horizons		-
Mixed feelings about technology		Learning opportunities		-		-		Positive emotional impact

Discussion

In our study, we observed significant improvements in cognitive scores among elderly participants, aligning with findings from previous research.[16] The results indicate that active engagement with technology, including online activities and computer use, holds the potential to enhance cognitive functions in the elderly. Utilizing cognitive tests as a quantitative measure, we found that technology use can positively impact memory and overall mental abilities, emphasizing the cognitive benefits associated with technological engagement in elderly care centers. Examining the patterns of social media and computer use revealed diverse typologies among elderly users in care centers. These findings resonate with research by Bixter et al.,[15] emphasizing the individualized nature of technology adoption among the elderly. Some participants attempted such actions as communication, information-searching, and entertainment using technologies, and the others were easy to be caught in the weakness of limited use. Healthcare professionals must be aware and be able to provide specialized nursing care that addresses the needs of elderly people of various backgrounds or nationalities. The standard theme report from interviews enhanced the holistic perception of both the good and less beneficial sides of technology among the elderly in the institutions’ care. The ideas including “Improved Social Linkage” and “Information Access” wrote a story of how technology played a role in facilitating social relations and knowledge purposes. On the bright side, topics such as “Technological Barriers” emerge as a sign of obstacles that may show up. This result supports the mentioned studies regarding informatization by older people[14] which suggest the necessity of the treatment of obstacles and promote informatization which is universal, useful, and beneficial for the elderly. Identifying and surpassing these obstacles is the very apparent need to discriminate against prohibited. Hence, elderly citizens in care institutions get better service.

The bidirectional hypothesis was supported by the substantial correlation found between improved memory function and increased technology use at subsequent times. These results support other research[31] that found memory to be a significant predictor of Internet use, indicating the necessity of intact memory for technology use. The unidirectional link was evident in the fact that the executive function did not pred-technology technology use in the following years. As our study observed, episodic memory might be more significant when it comes to the quantitative components of technology use. For older persons in particular, most technological applications necessitate a new learning process that utilizes episodic memory. However, the executive function may have more to do with the qualitative features of technology use (e.g. the technological strategies employed by successful people). Our results imply that, depending on the specific subdomain consideration, the link between technology use and cognitive function may vary. To understand the underlying mechanisms that link some cognitive domains to technology use and not others, more research is required. The lack of a correlation between executive functioning and technology use may have numerous causes.

The results showed that the elderly use technology for information-seeking, entertainment, social connection, and social connection. This indicates that technology has a positive impact on elderly health and psychology. The findings of the interview showed that technology engagement in-memory functionality as they believed technology has a positive impact on memory and enhances social connection. The technology could be used among the elderly for social connection and entertainment, information sharing, and information seeking among them to share information on health issues. The elderly seek entertainment and leisure as they have a plethora of time to spend alone in care centers as technology impacts their daily activities.

One theory is that results from abstract executive function tests might not translate to tasks in everyday life. Briegas et al.[32] discovered that in healthy older people, subpar performance on abstract executive tasks was not linked to subpar problem-solving abilities in real life. The executive deficits of older persons, as per the authors, could be offset by an increase in experience and knowledge with age. Furthermore, executive functioning may not be adequate for technological device use and appropriate/efficient technology use may require additional elements such as experience or technological knowledge and attitude (e.g. intention to use).[33] Shikha et al.,[34] surveyed memory assistive technology for the elderly as they describe the correlation between a person’s readiness for technology and its perceived utility and ease of use, which collectively lead to use intention. Hence, the analysis of the relationship between aging and technology adoption must consider both cognitive capacity and attitudes. Therefore, this study, based on the outcomes, adds to knowledge by showing that, depending on the cognitive domain, there may be a different direction of association between technology use and cognitive performance. The study outcomes hold significance for caregivers, policymakers, and researchers by offering a deepened comprehension of the cognitive aspects of the elderly in care. To further contextualize the study, it is essential to underscore its significance in theoretical, practical, and methodological terms. Therefore, this study offers practical results to elderly care centers in Jordan on the effect of technology on elderly memory functionality.

Limitation and recommendation

Although the study provides useful information, recognizing its shortcomings is a necessity. The sample size (though representative) might have failed to capture the diverse nature of the elderly individuals in the care centers. Further research should be targeted at bigger and more diverse samples to increase the transferability of results. Furthermore, the design of the study being cross-sectional hinders us from concluding causal relationships. Studies along the longitudinal dimension, examining the effect of continued technology use on cognition and memory over time will provide a deeper understanding of the nature of the dynamics. The practical recommendations of the study are useful for caregivers, and healthcare professionals in enhancing memory retrieving and memory functionality among the elderly. In addition, technology developers in geriatric mental healthcare. Tailored interventions should be created to cater to the diverse levels of technological knowledge and preferences that are displayed by the elderly. Training programs oriented on removing technological constraints and resolving privacy issues could also increase technology integration into care operations. The cooperation of healthcare providers and technology developers is essential for designing user-friendly platforms which fit the individual specific needs and preferences of the elderly. The summary of this study presents the findings, giving the cognitive benefits of technology use, mapping patterns of social media and computer usage, and suggesting the barriers and possibilities in implementing technology for the elderly. The implications discussed present a base for future research and interventions that will maximize technology utilization for geriatric mental healthcare enhancement. Future studies might conduct experimental design to offer deeper implications using a larger sample. it is also suggested to investigate the impact of technology on participants with memory disease.

Conclusion

The purpose of this research is to emphasize the medical and psychological consequences of late-life memory loss, which are underlying the field of geriatric mental health care. The intrusion of technology, especially the use of social media as well as internet-based therapies, the biggest factor of all, is in-depth engagement in the cognitive function and psychological well-being of the elderly in the care centers. Identifying the mediators in the technology adoption process, which entails personal characteristics, cultural context and adaptation problems, is of utmost importance for the development of interventions that are based on context and respond to the unique needs of people. By acknowledging these considerations, healthcare practitioners and policymakers can foster an environment that optimizes the positive impact of technology on the mental health of the elderly, paving the way for a more inclusive and effective geriatric mental healthcare landscape.

Ethical consideration

After the approval of the research project, an ethics code was obtained from the ethics committee of the university (IRB. UJ. 102-2023). All participants provided their informed written consent, and they were made aware of the research process and were assured of the confidentiality of their information. In addition, they were allowed to withdraw at any stage during the study.

Conflicts of interest

There are no conflicts of interest.

Funding Statement

Nil.