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Abstract

The Edmonton Seniors Centre Without Walls program provides free health, psychosocial, and educational telephone programming for older adults who experience multiple barriers to traditional in-person programming. The aim of this program evaluation was to assess outcomes of participation using validated scales of loneliness and psychosocial and health quality of life. Telephone interviews were conducted pre (n = 160) and post (n = 99) with participants. Given the variation in average attendance, results were assessed by level of participation: Low, Moderate, and High Users. There was statistically significant improvement in all participants’ attitudes towards their self-realization and towards energy levels, and EQ-5D-5L anxiety/depression scale after participation, along with a significant reduction in feelings of social isolation. The highest rates of improvement were seen within High Users. These findings suggest that telephone-based programs could be a useful intervention to improve the wellbeing and socially connectedness of older adults.

Keywords

social support loneliness psychosocial older adults telephone programming

While today’s older adults are more active and healthier than previous generations, they are also more likely to be living with chronic conditions, disabilities, and mental health issues that put them at greater risk for social isolation (Government of Canada, 2014). Given this and the COVID-19 pandemic, the issue of social isolation has become an increasingly important topic for the future of aging. While this is an issue across the lifespan, older adults are at increased risk of becoming socially isolated, with studies suggesting the highest prevalence in those over the age of 80 (Demakakos et al., 2006; Dykstra, 2009). One in five Canadians aged 65 or older indicated that they felt lonely some of the time or often (Statistics Canada, 2012). While isolation may be by choice or by circumstance, the experience is subjective and based on the individuals’ perception. For the purpose of this study, the following definitions are used. Loneliness is the subjective experience of distress over not having enough social relationships or not enough contact (Lopata, 1969; Weiss, 1973). Social Isolation refers to an objective state rather than a subjective experience (Cohen–Mansfield & Perach, 2015). Social isolation often arises in situations where a person does not have enough people to interact with or enough social support, usually indicated by a small social network or a low number of regular social interactions (Chappell & Badger, 1989).

Loneliness and social isolation have gained increasing attention as social determinants of health given their associated physical/mental health risks. Their impacts are comparable to those of other health risk factors including physical inactivity, smoking, and obesity (MacLeod, et al., 2018). Older adults who are lonely have an increased risk of dying sooner and are more likely to experience a decline in their mobility, compared to those who are not lonely (Luo et al., 2012). Having supportive social relationships is related to decreased mortality risk (MacLeod, et al., 2018). Loneliness is related to poor subjective health (Cornwell & Waite, 2009; Havens, et al., 2004), multiple chronic illnesses (Theeke, 2010), impaired hearing (Victor et al., 2005), and poor self-reported functioning (Savikko et al., 2005). Loneliness can increase an individual’s risk of depression, anxiety (Golden, et al., 2009; MacLeod, et al., 2018; Tiikkainen & Heikkinen, 2005), and dementia (Luo et al., 2012; Wilson, et al., 2007). Loneliness is also a predictor of suicide among older adults aged 65 and older (Waern et al., 2003). Living in social isolation can also reduce self-esteem & confidence, social skills, and increase the risk of elder abuse (NICE: National Institute for the Care of the Elderly, 2015).

Experiences of social isolation are often complex and multifactorial, and poor health, chronic illnesses and disabilities can further marginalize older adults and make it harder to stay socially connected. There are many factors that can influence an older adult’s risk of social isolation including marital status, living situation, education, and financial status (Cudjoe, et al., 2020). Barriers such as lack of resources including transportation, and lack of appropriate opportunities for social connection can predict loneliness (Cohen–Mansfield & Parpura–Gill, 2007).

The peer-reviewed literature on interventions’ impacts on loneliness and isolation suggests that educational and psychosocial group interventions can help alleviate social isolation and loneliness in older adults (Cattan et al., 2005). Specifically, remote interventions that used technology to alleviate loneliness were found to be potentially effective within group formats, for older adults living both in community and institutions (Cohen–Mansfield & Perach, 2015). These interventions used multiple technological features including email programs, videoconference interaction with staff and family, as well as computer training for older adults (Cohen–Mansfield & Perach, 2015). However, there is a challenge in finding high quality evidence of impacts of loneliness and isolation interventions (Cattan et al., 2005; Cohen–Mansfield & Perach, 2015). Despite recent advances in technology usage, older adults may be reluctant to adopt and can be non-adherent in using online technology (Anderson & Perrin, 2017; Seifert et al., 2020). There are potential barriers to adoption that may explain the reluctance: concerns about privacy, access to the hardware and internet (Anderson & Perrin, 2017), socioeconomic, education, employment, and cohabitation status (Seifert et al., 2020; Hargittai & Dobransky, 2017). Age-related changes to physical and mental capability can be an impediment to technology usage, which many designers of hardware and software do not take into sufficient consideration (Hargittai & Dobransky, 2017). It is important then to select the right platform for remote programing that targets a diverse older adult population, while ensuring accessibility and efficient delivery of programming.

Telephone programming has been identified as a potential intervention for social isolation within Canada (Government of Canada, 2017). Evaluations of individual telephone programs have demonstrated some positive results (Newall & Menec, 2015) with the evidence in reviews indicating potential effectiveness (Stewart, et al., 2001) or inconclusive (Swindell & Mayhew, 1996). However, these evaluations have been limited to post-intervention questionnaires, based on participant self-reporting of perception of impact with limited sample sizes (ranging from 18 to 26). This purpose of this program evaluation was to add to the literature by utilizing validated loneliness and quality of life scales, pre-post intervention, to more clearly articulate if the services could be effective in improving participant outcomes with a larger sample.

Methods

This study was part of an evaluation process, which also provided insight into whether the program was reaching its target population, streamlining operations, and identifying gaps in service delivery and opportunities for improvement.

Program Background

In 2016, our organization was awarded a 3 year grant from the Government of Canada New Horizons for Seniors funding program, to develop a Seniors’ Centre Without Walls (SCWW) based out of primary care in Edmonton, Alberta, Canada. The concept of a SCWW was first conceived by the Dorot University in New York in 1989 (Dorot USA, 2021), and has since spread to other jurisdictions across North America. This program is also the only SCWW in North America based out of a healthcare organization, not a community social services organization, allowing for better referral pathways within primary care and more access to regular health speakers and programming, including behavioral health consultants, exercise specialists, nurses, dietitians, and respiratory therapists.

A SCWW is a telephone-based program offering a variety of interactive programming, free of charge to those aged 55 years and older. If a person requests to join and they are younger than 55, but are experiencing barriers to participating in traditional programming, they are not denied service. The goal is to support social connections for older adults who are homebound and/or isolated, through building an inclusive community atmosphere and ensuring the accessibility of social opportunities. Programming does not require any specialized equipment for participants other than a standard touch-tone or cellphone. The low-technological program provides an alternative to having older adults attend in-person programming at seniors’ centers. Free conference calls allow the older adults to join in small groups (7–10 participants on average), led by a facilitator. Participants included community-dwelling older adults and those living in institutions. Participants could self-refer, and referrals were accepted from caregivers, healthcare professionals, and community workers. The program was promoted through a local seniors’ newspaper and through local seniors-serving organizations’ newsletters. The project staff connected with incoming participants over the phone to provide additional understanding of the program and complete the registration. For each series, program schedules and supplementary materials based on program content were mailed out in participant packages. Participants chose which programs to join based on their availability and interests. Programming topics fell under three main categories: health and wellness information (e.g., chronic disease management, relaxation, exercise); recreational activities (e.g., BINGO, book clubs, museum talks, world news); and opportunities for skill building and learning (e.g., English practice, arts and culture presentation, home decluttering). The majority of programs had participatory elements with discussions, games, and activities to complete at home, alongside sessions with more traditional lecture style presentations and guest presenters. On average, the program offered two hour-long programs a day, Monday through Friday, at various times throughout the week.

Data Collection Procedure

Pre-Post data was collected through telephone surveys, between November 2016 and April 2019 (Figure 1). Participants joined the program (through both referral and self-enrollment) on a continuous basis and completed intake over the telephone with SCWW program staff to collect demographic information, risk factors of isolation, and initial responses to the validated scales. Our study excluded participants who were unable to give verbal responses to the required questions in English. The organization hired an evaluation assistant to act independently from the program staff and to complete follow-up with participants 6 months after entry into the program. Participants were invited to complete the telephone survey and were given the right to refuse any questions they did not want to answer or to opt out of the survey entirely.

Data Collection Instruments

Demographic information was collected through the intake survey including the participant’s age, gender, marital status, living arrangement, monthly income, caregiving status, primary language, general health, and vision or hearing problems. Additionally, participants were asked about their contact with friends/family, what if anything prevented them from social participation, and whether they have computer or internet access.

Participants completed the three-item version of the Revised University of California Los Angeles (UCLA) loneliness scale (Hughes et al., 2004), which enquires about frequency of feeling left out, isolated, and lacking companionship. Each question is rated on a 3-point scale: 1 = Hardly Ever; 2 = Some of the Time; 3 = Often. The scores for each individual question are combined to give a possible range of scores from 3 to 9, with higher values indicate greater loneliness. UCLA loneliness scale can be reported by grouping people who score 3–5 as “not lonely” and people with the score 6–9 as “lonely.”

Participants also completed the CASP-12 tool, based on 12 items, which assess four theoretically derived dimensions of Quality of Life (QoL) in older adults: control, autonomy, self-realization, and pleasure (Higgs et al., 2003). The CASP-12 tool is used to measure the psychosocial constructs of quality of life. The items are presented as questions or statements and are assessed on a 4-point Likert scale (“often,” “sometimes,” “rarely,” “never”). This outcome was analyzed as a continuous variable (score range of 12–48), with lower scores associated with poorer QoL (Borrat–Besson et al., 2015; Palgi et al., 2015).

The final validated tool completed was the EQ-5D-5L, a survey assessing five health dimensions with varying levels of severity and overall health visual analog scale (VAS) (EuroQol Research Foundation, 2020). The five dimensions include mobility, self-care, usual activities, pain or discomfort, and anxiety/depression. The varying levels of severity range from one to five, where one represents having no problems and five represents extreme problems. The EQ-5D-5L VAS has respondents indicate their current health status by choosing a number from 0 (worst imaginable health state) to 100 (best imaginable health state). An EQ-5D-5L Index score is calculated from responses to questions in all five domains and assessed based on the Canadian scoring algorithm range from −0.148 to 0.949. Higher scores indicated better health-related quality of life, and scores < 0 indicating a health state “worse than dead,” scores = 0 “same as dead,” and scores > 0 “better than dead” (McClure, et al., 2017).

Given the large variation in average monthly attendance (between 1–14 visits, average three visits per month), participants were classified by their average level of participation and calculated using participant attendance records. Low Users attended less than 3 times in 3 months, Moderate Users attended between three but less than 12 times in 3 months, and High Users attended at least 12 times during 3 months time period.

Statistical Analysis Methods

Data was analyzed in SPSS 24 (IBM Corporation). Descriptive statistics were used to characterize the participants. Wilcoxon Signed Rank Test and repeated Measures ANOVA were conducted to determine whether there were statistically significant group differences pre-post program. Mann–Whitney test were performed for group comparisons and paired Chi square test were performed for association testing. p-values below 0.05 were considered to be statistically significant.

Results

Overall, 160 participants completed an intake with the program between November 2016 and April 2019. The sociodemographic characteristics of the participants are presented in Table 1. The mean age of the study participants were 77 years (SD=8.9 years), the youngest 51 and the oldest 96 years. 91% of the participants were female and 49% lived with a spouse and/or family. The majority (81%) identified that they have family members, friends or neighbors that they have contact with at least once a week. 80% of the participants identified that they faced barriers that stop them from participating in activities or social functions outside of their home. Obstacles reported included transportation difficulties (54%), vision problem (35%), safety (27%), hearing problem (22%), and concern with housing (22%). Only 53% of the participants had access to a computer and the internet. 84% of the participants indicated English as their primary language.

Table 1.

Demographics of Participants at Baseline by User Level.

		Total	LowUsers	Moderate Users	High Users	Significancep<0.05
N		160	53	81	26
Age	Mean (SD)	77 (8.9)	76 (9.1)	77 (9.4)	76 (7.5)	n.s.
Age	min-max	51–96	53–96	51–93	64–94	n.s.
Gender	Female	91%	89%	91%	96%	n.s.
Gender	Male	9%	11%	9%	4%	n.s.
Marital status	Married	28%	38%	19%	36%	n.s.
	Widowed	47%	48%	53%	23%
	divorced/Single	25%	14%	28%	41%
Living arrangement	Living alone	51%	34%	63%	50%	n.s.
Living arrangement	Live with spouse and/or family	49%	66%	37%	50%	n.s.
Monthly income	Over $2,200	19%	10%	19%	25%	n.s.
	$1,600–$2,200	16%	20%	16%	13%
	Under $1600	27%	30%	20%	43%
	Prefer not to answer	38%	40%	44%	19%
Taking care of another person	Yes	8%	7%	10%	4%	n.s.
Taking care of another person	No	92%	93%	90%	96%	n.s.
Contact with friends/family at least once a week	Yes	81%	86%	80%	77%	n.s.
Contact with friends/family at least once a week	No	19%	14%	20%	23%	n.s.
English as primary language	Yes	84%	60%	95%	96%	p<0.05
English as primary language	No	16%	40%	5%	4%	p<0.05
Prevented from social participation	Yes	80%	79%	80%	82%	n.s.
Prevented from social participation	No	20%	21%	20%	18%	n.s.
Concern with housing	Yes	22%	18%	21%	32%	n.s.
Concern with housing	No	78%	82%	79%	68%	n.s.
Vision problem	Yes	35%	36%	34%	36%	n.s.
Vision problem	No	65%	64%	66%	64%	n.s.
Hearing problem	Yes	22%	18%	21%	32%	n.s.
Hearing problem	No	78%	82%	79%	68%	n.s.
Transportation difficulties	Yes	54%	57%	51%	59%	n.s.
Transportation difficulties	No	46%	43%	49%	41%	n.s.
Safety concerns	Yes	27%	29%	23%	36%	n.s.
Safety concerns	No	73%	71%	77%	46%	n.s.
Has computer or internet access	Yes	53%	55%	48%	68%	n.s.
Has computer or internet access	No	47%	45%	52%	32%	n.s.
General health	Excellent/Very	65%	73%	61%	44%	n.s.
	Good/Good
	Fair/Poor	35%	27%	39%	56%

One-third of the participants were low users and less than one fifth of the registrants were high program users who attended the programs at least once a week (Table 2). While user status was calculated by frequency of use, Low Users tended to stay in the program for a shorter duration overall, compared to Moderate and High Users. There were no statically significant differences in demographic factors collected between the user levels, except for English as primary language. English language proficiency by itself cannot explain differences between the groups, with no differences found between Moderate and High users. A participation trend, which may help us understand this difference, is that many of the attendees of our English Language practice groups only attended those limited sessions within the program.

Table 2.

Registration and Attendance Trends by Participant Group.

Participation Level	Low User (n=53)	Moderate User (n=81)	High User (n=26)
Total programs attended Mean (SD)	2 (1.4)	12 (10.0)	95 (95.2)
Total programs registered Mean (SD)	4 (4.4)	25 (19.9)	115 (101.5)
Average length of participation Months (SD)	1 (0.6)	4 (3.1)	9 (7.9)
Still participating in SCWW program 6 months after starting N (%)	7 (33%)	36 (63%)	19 (91%)

UCLA Loneliness Scale

One-third (33%) of the participants reported improvement on the total UCLA loneliness score. This improvement in the total UCLA loneliness score was statistically significant for High Users of the program (Figure 2). Overall, participants reported a reduction in their feelings of isolation between baseline and follow-up measurements (2.1 vs. 1.9; p < 0.05).

Figure 1.

Participant retention flowchart

CASP—12

Participants reported less concerns than at baseline, with 36% of the participants showing improvement in the CASP-12 scores at follow-up. There was no change to nine of the domains, however, there was statistically significant improvement (p < 0.05) in the participants’ attitude towards their self-realization (Q: I feel left out of things) and towards their energy levels (Q: I feel full of energy these days). There was statistically significant decline (p < 0.05) in the participants’ attitude towards their finances (Q: I do not have enough money to do the things I want to do) Figure 3.

Figure 2.

Overall UCLA loneliness score by User Level (lower score—least lonely)

EQ-5D-5L

On average, participants had lower health states and higher anxiety levels at baseline and follow-up measurements, compared to the Alberta norms for those 50 years and older (Health Quality Council of Alberta, 2014) (see Table 3).

Table 3.

Comparison of EQ-5D-5L Scores.

		Alberta Norms for 50+ (%)	SCWW (Baseline) % (n=98)	SCWW (Follow-up) % (n=98)
Mobility	No problems	60.0	19.4	24.5
	Slight problems	22.6	24.5	21.4
	Moderate problems	11.1	26.5	31.6
	Severe problems	5.3	22.4	20.4
	Unable to	1.1	7.1	2.0
Self-care	No problems	91.5	46.9	53.1
	Slight problems	5.8	22.4	21.4
	Moderate problems	1.9	22.4	16.3
	Severe problems	0.5	5.1	8.2
	Unable to	0.5	3.1	1.0
Usual activities	No problems	67.8	28.6	26.5
	Slight problems	19.4	24.5	31.6
	Moderate problems	10.2	33.7	32.7
	Severe problems	1.4	10.2	9.2
	Unable to	1.3	3.1	0.0
Pain/discomfort	No pain	30.5	23.5	14.4
	Slight pain	42.1	17.3	24.7
	Moderate pain	21.3	30.6	33.0
	Severe pain	4.5	22.4	15.5
	Extreme pain	1.7	6.1	10.3
Anxiety/depression	Not anxious	72.4	30.9	41.2
	Slightly anxious	19.0	18.6	23.7
	Moderately anxious	7.1	36.1	27.8
	Severely anxious	1.0	12.4	3.1
	Extremely anxious	0.6	2.1	4.1

Overall, 39% of participants reported improvement on at least one EQ-5D-5L scale. The highest improvement was on the Anxiety/Depression scale (Figure 4) and this change was statistically significant (2.36 vs. 2.05, p<0.05). Half of the participants reported improvement in EQ-5D-5L Index scores and 35% reported improvement in EQ-5D-5L VAS scores. There were no differences between different levels of participant engagement. Changes in EQ-5D-5L Index and VAS scores were not statistically significant. There was a trend for improvement in EQ-5D-5L Index score (0.6 vs. 0.64, p = 0.06) which may be clinical significant, as it meets the established minimal clinical important differences of 0.04 points for the EQ-5D Index (McClure, et al., 2017).

Discussion

At the beginning of this project, social isolation among older adults was a major concern for older adults. The need for social distancing during COVID-19 has exacerbated this problem, as almost all older adults regardless of living situation have experienced reduced social connections. Our participants indicated a greater degree of loneliness at the baseline and follow-up measurements, compared to literature rates (Luo et al., 2012). Average values for UCLA reported by ELSA Study was 4.06 ± 1.47 (Steptoe et al., 2013), while the SHARE study reported 4.29 (Luo et al., 2012). Our participants indicated lower quality of life as measured by CASP-12, compared to literature rates (Ateca-Amestoy & Ugidos, 2013) as well as higher than average health and environmental concerns. This data would suggest that the program is reaching its intended population of isolated older adults, experiencing health, mobility, and other challenges.

Previous evaluations favored more qualitative responses, focusing on the satisfaction with the program and perceived impacts (Newall & Menec, 2015; Stewart, et al., 2001). Most reported that participating older adults self-reported feeling more connected and less lonely after participation (Newall & Menec, 2015; Stewart, et al., 2001). Both studies identified emotional support from the telephone groups was a significant benefit of the intervention, which was reinforced by our participants’ comments where many shared they had made strong and meaningful relationships with the SCWW staff and made new friends on the program, with a number of participants referred to SCWW as their “only family.”

The results of our evaluation suggests a SCWW could reduce feelings of isolation as reported on the UCLA loneliness scale and anxiety/depression as reported on the EQ-5D-5L. Our evaluation also indicates that participation in SCWW programming can have a statistically significant improvement on feelings of self-realization and energy levels. While not statistically significant, there was a trend post-intervention for improvement on the EQ-5D-5L Index score, lower loneliness scores on the UCLA scale and for reporting fewer concerns on the CASP-12, which might still have clinical importance, and provide useful direction for intervention (Miller, 2007). In addition, the impact of the program can vary depending on the engagement level. High Users as a group reported the highest improvement at follow-up measurement, in feelings of connection and total UCLA loneliness score.

Limitations of the study

As there were participants lost to follow-up, we cannot make conclusions that the program was effective for every participant. While we tried to identify the reason why participants were lost to follow-up, detailed in Figure 1, we did see more lost to follow-up in the low participant category, potentially indicating an overall ineffectiveness of the program for these participants. Our participants were also predominately female and preventing us from comparing effectiveness of program by gender, though comparable proportions where reported in other evaluations of similar programs (Newall & Menec, 2015; Swindell & Mayhew, 1996; Stewart, et al., 2001). Whether this program is more acceptable and effective with female participants than males should be explored future evaluations.

Figure 3.

CASP-12 Baseline vs. Follow-up Participant Responses

Figure 4.

EQ-5D-5L score change by Dimensions.

This is a prospective study design—a pre/post evaluation designed to monitor participant changes over time. Supplementing this study with a control group (participants of similar demographics not enrolled in the SCWW program) would allow for more reliable comparison of the impact however was not appropriate, as it would involve identifying but not providing support for isolated older adults. Another limitation would be self-selection bias, as we did not assign individuals to levels of use, rather observed their participation patterns and impacts. It is possible that those who were most likely to benefit from the program self-selected into a higher use group. There was a higher representation of English as primary language speakers in the Moderate and High users groups. English language proficiency was not objectively measured by this evaluation and the impact of programming in other languages should be explored in the future.

Conclusions

The SCWW model could be a tool to help improve the wellbeing of isolated and homebound older adults by connecting them to a community, utilizing alternative technology to access the programs and services. It is important to recognize that many older adults do not have regular access to the internet or other web-based technology. Seniors-serving organizations should explore low-technology options to expand their offerings when trying to reduce the impacts of social isolation.

Footnotes

Acknowledgments

Thank you to Sandra Gad and Gayle Harper for their assistance on this project.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported in part by the Government of Canada’s New Horizons for Seniors’ grant (project number 1364716) and supported by the Edmonton Southside Primary Care Network.

ORCID iD

Heather Roland

References

Anderson

Perrin

(2017). Tech adoption climbs among older adults. Pew Research Center. https://www.pewresearch.org/internet/2017/05/17/technology-use-among-seniors/.

Ateca-Amestoy

Ugidos

(2013). The impact of different types of resource transfers on individual wellbeing: An analysis of quality of life using CASP-12. Social Indicators Research, 110(3), 973-991. https://doi.org/10.1007/s11205-011-9968-5.

Borrat-Besson

Ryser

V. A.

Gonçalves

(2015). An evaluation of the CASP-12 scale used in the survey of ageing and retirement in Europe (SHARE) to measure quality of life among people aged 50+. FORS Working Paper Series, 2015(4). https://doi.org/10.24440/FWP-2015-00004

Cattan

White

Bond

Learmouth

(2005). Preventing social isolation and loneliness among older people: A systematic review of health promotion interventions. Ageing and Society, 25(1), 41–67. https://doi.org/10.1017/S0144686X04002594

Chappell

N. L.

Badger

(1989). Social isolation and well-being. Journal of Gerontology, 44(5), S169–S176. https://doi.org/10.1093/geronj/44.5.s169

Cohen-Mansfield

Parpura-Gill

(2007). Loneliness in older persons: a theoretical model and empirical findings. International Psychogeriatrics, 19(2), 279–294. https://doi.org/10.1017/S1041610206004200

Cohen-Mansfield

Perach

(2015). Interventions for alleviating loneliness among older persons: a critical review. American Journal of Health Promotion, 29(3), e109–e125. https://doi.org/10.4278/ajhp.130418-LIT-182

Cornwell

E. Y.

Waite

L. J.

(2009). Social disconnectedness, perceived isolation, and health among older adults. Journal of Health and Social Behavior, 50(1), 31–48. https://doi.org/10.1177/002214650905000103

Cudjoe

Roth

D. L.

Szanton

S. L.

Wolff

J. L.

Boyd

C. M.

Thorpe

R. J.

(2020). The epidemiology of social isolation: National health and aging trends study. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 75(1), 107–113. https://doi.org/10.1093/geronb/gby037

10.

Demakakos

Nunn

Nazroo

(2006). Loneliness, relative deprivation, and life satisfaction. In Banks

Breeze

Lessof

Nazroo

Retirement

Health

(Eds), Relationships of the Older Population in England: The 2004 English Longitudinal Study of Aging. Institute for Fiscal Studies.

11.

Dorot USA (2021). University without walls. https://www.dorotusa.org/our-programs/at-home/university-without-walls

12.

Dykstra

P. A.

(2009). Older adult loneliness: Myths and realities. European Journal of Ageing, 6(2), 91–100. https://doi.org/10.1007/s10433-009-0110-3

13.

EuroQol Research Foundation (2020). EQ-5D-5L About. https://euroqol.org/eq-5d-instruments/eq-5d-5l-about/

14.

Golden

Conroy

R. M.

Bruce

Denihan

Greene

Kirby

Lawlor

B. A.

(2009). Loneliness, social support networks, mood and wellbeing in community-dwelling elderly. International Journal of Geriatric Psychiatry, 24(7), 694–700. https://doi.org/10.1002/gps.2181

15.

Government of Canada (2014). The National Seniors Council’s report on the social isolation of seniors 2013-2014. https://www.canada.ca/en/national-seniors-council/programs/publications-reports/2014/social-isolation-seniors.html

16.

Government of Canada (2017). Who’s at risk and what can be done about it? A review of the literature on the social isolation of different groups of seniors. https://www.canada.ca/en/national-seniors-council/programs/publications-reports/2017/review-social-isolation-seniors.html.

17.

Hargittai

Dobransky

(2017). Old dogs, new clicks: Digital inequality in skills and uses among older adults. Canadian Journal of Communication, 42(2), 195–212. https://doi.org/10.22230/cjc.2017v42n2a3176

18.

Havens

Hall

Sylvestre

Jivan

(2004). Social isolation and loneliness: Differences between older rural and urban Manitobans. Canadian Journal on Aging, 23(2), 129–140. https://doi.org/10.1353/cja.2004.0022

19.

Health Quality Council of Alberta (2014). Alberta population norms for EQ-5D-5L (p. 2014). https://www.hqca.ca/wp-content/uploads/2018/05/2014_EQ_5D_5L_report_FINALFINAL-1.pdf

20.

Higgs

Hyde

Wiggins

Blane

(2003). Researching quality of life in early old age: The importance of the sociological dimension. Social Policy & Administration, 37(3), 239–252. https://doi.org/10.1111/1467-9515.00336

21.

Hughes

M. E.

Waite

L. J.

Hawkley

L. C.

Cacioppo

J. T.

(2004). A short scale for measuring loneliness in large surveys – results from two population-based studies. Research on Aging, 26(26), 655–672. https://doi.org/10.1177/0164027504268574

22.

Lopata

H. Z.

(1969). Loneliness: forms and components. Social Problems, 17(1), 248–262. https://doi.org/10.1525/sp.1969.17.2.03a00090

23.

Luo

Hawkley

L. C.

Waite

L. J.

Cacioppo

J. T.

(2012). Loneliness, health, and mortality in old age: a national longitudinal study. Social Science & Medicine, 74(6), 907–914. https://doi.org/10.1016/j.socscimed.2011.11.028

24.

MacLeod

Musich

Parikh

R. B.

Hawkins

Keown

Yeh

C. S.

(2018). Examining approaches to address loneliness and social isolation among older adults. Journal of Aging and Geriatric Medicine, 2(1), ■■■. https://doi.org/10.4172/2576-3946.1000115

25.

McClure

Al Sayah

Xie

Luo

Johnson

J. A.

(2017). Instrument-defined estimates of the minimally important difference for eq-5d-5l index scores. Value Health, 20(4), 644–650. https://doi.org/10.1016/j.jval.2016.11.015

26.

Miller

J. D.

(2007). Finding clinical meaning in cancer data. Journal of the National Cancer Institute, 99(24), 1832–1835. https://doi.org/10.1093/jnci/djm283

27.

Newall

N. E.

Menec

V. H.

(2015). Targeting socially isolated older adults: A process evaluation of the senior centre without walls social and educational program. Journal of Applied Gerontology, 34(8), 958–976. https://doi.org/10.1177/0733464813510063

28.

NICE (2015). National Institute for the Care of the Elderly. In Into the light: National survey on the mistreatment of older Canadians 2015. https://cnpea.ca/images/canada-report-june-7-2016-pre-study-lynnmcdonald.pdf

29.

Palgi

Shrira

Zaslavsky

(2015). Quality of life attenuates age-related decline in functional status of older adults. Quality of Life Research, 24(8), 1835–1843. https://doi.org/10.1007/s11136-015-0918-6

30.

Savikko

Routasalo

Tilvis

R. S.

Strandberg

T. E.

Pitkälä

K. H.

(2005). Predictors and subjective causes of loneliness in an aged population. Archives of Gerontology and Geriatrics, 41(3), 223–233. https://doi.org/10.1016/j.archger.2005.03.002.

31.

Seifert

Cotten

S. R.

Xie

(2020). A double burden of exclusion? Digital and social exclusion of older adults in times of COVID-19. Journals of Gerontology: Social Sciences, 76(3), e99–e103. https://doi.org/10.1093/geronb/gbaa098

32.

Statistics Canada (2012). Social participation and the health and well-being of Canadian seniors. http://www.statcan.gc.ca/pub/82-003-x/2012004/article/11720-eng.htm.

33.

Steptoe

Shankar

Demakakos

Wardle

(2013). Social isolation, loneliness, and mortality. Proceedings of the National Academy of Sciences, 110(15), 5797–5801. https://doi.org/10.1073/pnas.1219686110

34.

Stewart

Mann

Jackson

Downe-Wamboldt

Bayers

Slater

Turner

(2001). Telephone support groups for seniors with disabilities. Canadian Journal on Aging, 20(1), 47–72. https://doi.org/10.1017/S0714980800012137

35.

Swindell

Mayhew

(1996). Educating the isolated ageing: Improving the quality of life of the housebound elderly through educational teleconferencing. International Journal of Lifelong Education, 15(2), 85–93. https://doi.org/10.1080/0260137960150203

36.

Theeke

L. A.

(2010). Sociodemographic and health-related risks for loneliness and outcome differences by loneliness status in a sample of US. older adults. Research in Gerontological Nursing, 3(2), 113–125. https://doi.org/10.3928/19404921-20091103-99

37.

Tiikkainen

Heikkinen

R. L.

(2005). Associations between loneliness, depressive symptoms and perceived togetherness in older people. Aging & Mental Health, 9(6), 526–534. https://doi.org/10.1080/13607860500193138

38.

Victor

Scambler

Bowling

Bond

(2005). The prevalence of, and risk factors for, loneliness in later life: A survey of older people in Great Britain. Ageing and Society, 25(6), 357–375. https://doi.org/10.1017/S0144686X04003332

39.

Waern

Rubenowitz

Wilhelmson

(2003). Predictors of suicide in the old elderly. Gerontology, 49(5), 328–334. https://doi.org/10.1159/000071715

40.

Weiss

R. S.

(1973). Loneliness: The experience of emotional and social isolation. MIT Press.

41.

Wilson

R. S.

Krueger

K. R.

Arnold

S. E.

Schneider

J. A.

Kelly

J. F.

Barnes

L. L.

Bennett

D. A.

(2007). Loneliness and risk of alzheimer disease. Archives of General Psychiatry, 64(2), 234–240. https://doi.org/10.1001/archpsyc.64.2.234

Connection Through Calls: The Impact of a Seniors Center Without Walls on Older Adults’ Social Isolation and Loneliness

Abstract

Keywords

Methods

Program Background

Data Collection Procedure

Data Collection Instruments

Statistical Analysis Methods

Results

UCLA Loneliness Scale

CASP—12

EQ-5D-5L

Discussion

Limitations of the study

Conclusions

Footnotes

Acknowledgments

Declaration of Conflicting Interests

Funding

ORCID iD

References