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This article was written on 31 Oct 2013, and is filled under Volume 17 Number 3.

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Effects of Technology and Connectedness on Community-Dwelling Older Adults

By

Joan M. Culley, PhD, MPH, RN, CWOCN,

JoAnne Herman, PhD, RN,

David Smith, BSN, RN

  Abbas Tavakoli, DrPH, MPH, ME

Citation

Culley, J, Herman, J., Smith, D., & Tavakoli, A.  (October 2013). Effects of Technology and Connectedness on Community-Dwelling Older Adults. Online Journal of Nursing Informatics (OJNI), vol. 17(3), Available at  http://ojni.org/issues/?p=2864

Abstract

Purpose: To examine the relationship between technology and connectedness in community-dwelling older adults.

Methods: Survey was collected conducted using convenience sampling of 86 community-dwelling older adults to assess their use of technology and examine the association between use of technology and connectedness.

Findings: Results indicated that as age increases, activities with technology decrease (r=-0.43). Interest, skills and intent in using technology were weakly correlated with feelings of how to face aging, being spiritual and being part of a family. Participants did not believe that cost (69%), poor eyesight or physical impairments (88%) were barriers to using technology. Interestingly 35% of the subjects used the web to search for health information, 44% used the phone or computer to search information and 43% kept in touch with family online.

Conclusions: This study is a beginning in our understanding about the potential role of technology in improving connectedness and alleviating isolation in community-dwelling older adults.

Background and Significance

Baby boomers are retiring at a rate of 10,000 a day adding to the growing population of individuals 65 years of age and older (Cohn & Taylor, 2010). The 2010 US census data show that the number of older people is expected to increase from 13% of the population in 2011 to 20% by 2030 (http://www.epa.gov/aging). This represents a two-fold increase since 2000, growing from 35 million to 72 million (http://www.agingstats.gov/Main_Site/Data/2012_Documents/Population.aspx). This population poses a challenge for public health experts because of increasing rates of chronic diseases which increase healthcare costs (Barnette et al., 2012). In an effort to offset the cost of chronic diseases, an area of research called successful aging has been developed. This research has revealed that psychological health is as important, if not more important than physiological health (CDC & National Association of Chronic Disease Directors (2008). Register, Herman, and Tavakoli (2011) have proposed that connectedness in older adults fosters psychological health through active engagement in the world. The study suggests that technology may be an important intervention for increasing connectedness in older adults.

Several studies illustrated the importance of older adults using technology for social networking in order to feel more connected to friends and family thus enhancing psychological health (Opalinski, 2001; Nahm, Resnick, & Gaines, 2004). A study provided preliminary evidence that, “interactive computer use with the right training conditions increased client self-esteem and reduced depression” (Billipp, 2001, p. 139). Clark (2002) described the use of chat room interviews and online questionnaires as an intervention to increase social connectedness among community-dwelling older adults. Recent research by Aguilar, Boerema, and Harrison (2010) indicated that older adults felt that regular use of computers lead to decreased feelings of isolation. A study by Gatto and Tak (2008) is one of the few studies that discussed the perceptions of older adults regarding their use of technology. The study showed the potential usefulness of technology for searching health information, keeping up with friends and gaming.

This study builds on the previous research of Register and Herman (2011, 2006) who studied the phenomenon of connectedness in older adults. Our study was designed to examine which facets of technology were most used by older adults and which facets were correlated with connectedness.

Methods

A convenience sample of 86 community dwelling older adults were recruited from a hospital association auxiliary member quarterly meeting and a Lion’s Club meeting to complete a paper and pencil demographic questionnaire, Register Connectedness Scale for Older Adults (Register, Herman, & Tavakoli, 2011) and Smith Social Media/Technology Survey developed by the researcher. The Register Connectedness Scale for Older Adults measures five dimensions of connectedness: self regulating, facing aging, being part of a family, having friends, and being spiritual. It is a 86 item survey that ranks everyday life events such as spending time with a friend and driving a car on a Likert scale from 1 (not satisfied) – 4 (very satisfied) it also makes the distinction between how important the event is to the individual taking the survey, and how satisfied they are with the item. The Smith Social Media/Technology Survey was specifically developed for this pilot study to examine: 1) engagement in the use of technology (Table 1); 2) interest and skill in using technology and 3) intention to use or purchase technology within the next 6 months. Review of the technology survey to assure accurate representation of the content of interest was completed by two experienced researchers in the field of gerontology and informatics.

At the beginning of the recruitment meetings, a brief introduction by the researcher to the attendees described the purpose of the study, the inclusion criteria (community dwelling older adults age 65 and older, who could speak and read English), and their voluntary participation in the study. At the end of each meeting, the paper and pencil questionnaire and survey tools were distributed to attendees who met the inclusion criteria and volunteered to participate. Surveys were then collected and a code number was assigned to each survey to ensure anonymity. Data were entered into SAS ® 9.2 (SAS Institute Inc. Cary, NC). Descriptive and correlation statistical analyses were used to analyze the data.

Results

Demographic Variables

The sample was primarily white (78%), female (79%) with a high education level (82% with some college education) and little disability. Fifty-four percent were between age the group of 65-75 with an average age of 75 years old, and 74% earned greater than $25,000/household. Ninety-two percent lived in single family households (meaning that they either live alone or with their spouse). Fifty-one percent were married, and 82 % were retired.

Technology Variables

The Smith Social Media/Technology Survey included three sections: frequency and use with technology, interest and skills in using technology, and intention to use or purchase technology within the next 6 months. Frequency of use with technology (Table 1) indicated that 74% of the sample used a computer, 76% used a cell phone, 75% used the internet and 70% email. Only 10% of the sample used a website to search for family history.

Table 1: Frequency Distribution of Engagement in the Use of Technology (N=86)

Table 1

 

Interest and skills in using technology showed that 51% often or very often expressed interest in learning about new technology, only 32% were discouraged from trying new technologies due to cost, 38% felt that the lack of knowledge discouraged them from trying new technologies, and only 12% believed that physical impairments or poor eyesight discouraged them from trying out new technologies. Intention to use or purchase technology within the next 6 months revealed that 80% of the respondents indicated the intention of using a computer, but only 12% have the intention of purchasing one. Of the 93% that intend to use a cell phone only 20% intend to purchase a cell phone.

Table 2 shows the mean, standard deviation, min-max, Cronbach Alpha reliability coefficient, and number of items for age, connectedness and technology. The average of satisfaction for self regulating score, facing aging, being part of family, having friends, and being spiritual were 47.92, 32.66, 35.60, 18.14 and 19.20 respectively. Also, the technology survey average mean for total activity, total interest and skills, and total intention were 29.86, 9.55, and 5.14 respectively. Reliability was assessed by calculating Cronbach Alpha. The results indicate that the internal consistency coefficients for all subscales for connectedness were above 0.80. Also, the Cronbach alpha for total activity, total interest and skills, and total intention were 0.92, 0.60, and 0.56 respectively.

Table 2: Distribution of Variables According to N, Mean, Standard Deviation, and Minimum, Maximum Range, Cronbach Alpha reliability Coefficient, and Numberof Items of Connectedness and Technology Scales

Table 2

 

Person’s Correlation Coefficient was computed for age, education, income, and technology with the Register Connectedness Scale of satisfaction and importance for Older Adults (Table 3). The analysis demonstrated a moderate negative correlation between the total activity in technology and the age (r=-0.43), and a weak positive linear correlation between total activity and facing aging (satisfaction). The data showed a weak positive correlation between the sample’s interest and skills in technology, their feelings of facing aging (r=0.27), and being spiritual (r=0.30). There was also a weak positive correlation between the sample’s intention of using technology and being part of the family (r=0.24). The data showed a weak positive correlation between the sample’s interest and skills in technology and their ability to self regulate (r=0.23), feelings of how to face aging (r=0.24), and being spiritual (r=0.24). There was also a weak positive correlation between the sample’s intention of using technology and being part of the family (r=0.25), and being spiritual (r=0.32).

Table 3: Pairwise Pearson Correlation between Age, Education, Income, and Technology with Connectedness Register Scale (Satisfaction / Important) (N=86)

 Table 3

 

Discussion

Analysis of the technology variables showed that a majority of the participants did not believe that cost (69%), poor eyesight or physical impairments (88%) are barriers to using technology. These findings differ from a study completed by Echt and Burridge (2011) that found older adults were discouraged from using technology because of poor eye sight. Interestingly 35% used the web to search for health information, 44% used the phone or computer to search information and 43% kept in touch with family online. While previous research indicated that older adults are less likely to use technology and have less experience with technology (Aguilar, Boerema, and Harrison, 2010; Czaja, Charness, Fisk, 2006; Hertzog, Nair & Sharit, 2006; Morris, Goodman & Brading, 2007), 88% of the participants in this study indicated the intention of using a computer and only 16% said they had no interest in learning about new technology.

Results indicated that as age increases activities with technology decrease (r=-0.43) but interest, skills and intent in using technology were found to be weakly correlated with feelings of how to face aging, being spiritual and being part of a family. While these findings are not statistically significant they do warrant further investigation.

There were several limitations to this study. This was a pilot study with the use of a convenience sample. The sample was primary white (96%) and only 4% Hispanic, compared to the state average where 66% are white, 27% are Black, and 5% Hispanic (U.S. Census 2010). The educational level was very high and the average income was greater for their age group than the national average. Therefore, the sample does not reflect the demographics of South Carolina and cannot be generalized. Further research is needed with a more representative and larger sample to validate the Smith Social Medial/Technology Survey and to replicate the study. Many of the participants answered that they did not know or understand questions in the Smith Social Medial/Technology Survey. This may have reflected that the questions were vague or unclear indicating the need for further refinement and testing of the Survey.

Conclusions

This study is a beginning in our understanding about the potential role technology can play in improving connectedness and alleviating isolation in community-dwelling older adults. Technology has the potential to provide older persons with the ability to interact with family and friends as well as communicate and connect with healthcare providers in ways that can improve quality of life. Further research is needed to investigate methods for studying isolated older adults, the impact of technology on health care decisions and the ability of this intervention to empower older adults with their healthcare decisions. The next step in this line of research is to establish reliability and validity on the Smith Social Media/Technology Scale.

References

 Aguilar, A., Boerema, C., & Harrison, J. (2010). Meanings attributed by older adults to computer use. Journal of Occupational Science17(1), 27-33

Barnett, K., Mercer, S.W., Norbury, M., Watt, G., Wyke, S., & Buthri, B. (1012). Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. DOI:10.1016/S0140-6736(12)60482-6. Retrieved from http://press.thelancet.com/morbidity.pdf

Bartles, S.J., & Pratt, S.I., (2009). Psychosocial rehabilitation and quality of life for older adults with serious mental illness: recent findings and future research directions. Current Opinion in Psychiatry, 22(4), 381-385.

Billipp, S. (2001). The psychosocial impact of interactive computer use within a vulnerable elderly population: a report on a randomized prospective trial in a home health care setting. Public Health Nursing, 18(2), 138-145.Blaschke, C., Freddolino, P., & Mullen, E. (2009). Ageing and technology: a review of the research literature. British Journal of Social Work, 39(4), 641-656.

Centers for Disease Control and Prevention and National Association of Chronic Disease Directors. (2008). Issue Brief 1: What do the data tell us? The State of Mental Health and Aging in America. Atlanta, GA: National Association of Chronic Disease Directors; 2008. Retrieved from http://www.cdc.gov/aging/pdf/mental_health.pdf

Clark, D. (2002). Older adults living through and with their computers. CIN: Computers, Informatics, Nursing, 20(3), 117-124.

Cohn, D, & Taylor, R. (2010). Baby boomers approach 65 – glumly. Pew Research Social and Demographic Trends. Retrieved from http://www.pewsocialtrends.org/2010/12/20/baby-boomers-approach-65-glumly/

Czaja, S., Charness, N., Fisk, A., Hertzog, C., Nair, S., Rogers, W., & Sharit, J. (2006). Factors predicting the use of technology: Findings from the center for research and education on aging and technology enhancement (create). Psychology and Aging, 21(6), 333-352.Moore, S. L. (1997). A phenomenological study of meaning in life in suicidal older adults. Archives of Psychiatric Nursing, 11(1), 29-36.

Echt, K., & Burridge, A. (2011). Predictor of reported internet use in older adults with high and low health literacy: The role of socio-demographics and visual and cognitive function. Physical & Occupational Therapy in Geriatrics, 29(1), 23-43.

Gatto, S., & Tak, S. (2008). Computers, internet, and e-mail use among older adults: Benefits and barriers. Educational Gerontology, 34, 800-811.

Moore, S. L. (1997). A phenomenological study of meaning in life in suicidal older adults. Archives of Psychiatric Nursing, 11(1), 29-36.

Morris, A., Goodman, J., & Brading, H. (2007). Internet use and non-use: Views of older users. Universal Access in the information Society, 6(1), 43-57.

Nahm, E., Resnick, B., & Gaines, J. (2004). Testing the reliability and validity of computer-mediated social support measures among older adults: a pilot study. CIN: Computers, Informatics, Nursing, 22(4), 211-219.

Opalinski, L. (2001). Older adults and the digital divide: assessing results of a web-based survey. Journal of Technology in Human Services, 18(3/4), 203-221.

Register, M. E., & Herman, J. (2010). Quality of life revisited: The concept of connectedness in older adults. Advances in Nursing Science, 33(1), 53-63. doi: 10.1097/ANS.0b013e3181c9e1aa.

Register, M. E., Herman, J., & Tavakoli, A. (2011). development and psychometric testing of the register – connectedness scale for older adults.  Advances in Nursing Science, 34(1), 60-72. doi: 10.1002/nur.20415.

Register, M. E., & Herman, J. (2006). A middle range theory for generative quality of life for the elderly. Advances in Nursing Science, 29(4), 340-350.

Author Bios

Joan M. Culley, PhD, MPH, RN, CWOCN

Dr. Joan Culley is an Assistant Professor of Nursing at the University of South Carolina, Columbia and a retired Naval nurse corps officer. She earned a PhD in Nursing Informatics and Health Systems and is a board Certified Wound, Ostomy and Continence Nurse. She is an expert in the application of informatics to emergency preparedness, mass casualty triage model validation, and medical outcome measures and the PI on a NIH/NLM study titled Mass Casualty Validation Study (R21LM10833). She supervises undergraduate and graduate research projects related to emergency preparedness, informatics applications in nursing and wound/ostomy issues. She has published and presented papers related to these topics.

JoAnne Herman, PhD, RN

Dr. JoAnne Herman is a certified stress management educator and a Professor Emeriti from the University of South Carolina College of Nursing. Prior to her retirement she was Assistant Dean for Graduate Studies. She is the co-author of three books: Clinical Reasoning: The Art and Science of Critical and Creative Thinking, The Eight Step Approach to Teaching Clinical Nursing, and The Eight Step Approach to Student Success. She has received numerous awards for teaching excellence and is well published in the area of stress and health.

David Smith, BSN, RN

David Smith grew up in Columbia, SC. He attended the University of South Carolina where he earned his Bachelors in Nursing Science. He currently resides in Gastonia, NC where he works as an Emergency Department Nurse.

Abbas Tavakoli, DrPH, MPH, ME

Dr. Abbas Tavakoli currently works as Director of Statistical Laboratory /Faculty with College of Nursing at the University of South Carolina. Dr Tavakoli has worked with the office of research of the College of Nursing since 1992.  His job entails teaching statistics for nursing students, and assistance with research and statistical procedures. He has served as a data manager and research team member for three previous NIH-funded R01 grants and several smaller grants that have required data management, display and analysis plans. He has assisted principal investigators to collect, manage, analyze, and present high quality data. From his collaborative research efforts on those projects, he has co-authored a number of data-based articles in peer-reviewed journals.

 

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