Introduction Exploring the association between technology use and sleep health in older adults is important as digital engagement becomes integrated into society. Objective This study aimed to examine sleep health and its association with technology use in a population-based cohort of 60 years and older. Methods This cross-sectional, population-based study (2023) included 436 older adults from the Swedish National Study on Aging and Care, Blekinge (SNAC-B) population. These participants were sent questionnaires about their sleep, internet usage, Digital Social Participation (DSP), Technology Anxiety (TA), Technology Enthusiasm (TE), and use of information and communication technology. We used a multidimensional instrument, SATED, to measure sleep health. In this study, we conducted statistical analyses using the chi2 test, T-test, Pearson correlation, and backward linear and logistic regression. Results Our study found that older adults (60 years+) have a mean sleep health score of 7.40 (SD = 2.03). TE (<InlineEquation ID="IEq1"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq1.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="60" /> </InlineMediaObject> <EquationSource Format="TEX">\(r = 0.18\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq2"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq2.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="70" /> </InlineMediaObject> <EquationSource Format="TEX">\(p < 0.001\)</EquationSource> </InlineEquation>) and DSP (<InlineEquation ID="IEq3"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq3.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="60" /> </InlineMediaObject> <EquationSource Format="TEX">\(r = 0.14\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq4"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq4.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p = 0.004\)</EquationSource> </InlineEquation>) were positively associated with better sleep health, while TA (<InlineEquation ID="IEq5"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq5.gif" Format="GIF" Height="14" Rendition="HTML" Resolution="72" Type="Linedraw" Width="74" /> </InlineMediaObject> <EquationSource Format="TEX">\(r = -0.15\)</EquationSource> </InlineEquation>, <InlineEquation ID="IEq6"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq6.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p = 0.003\)</EquationSource> </InlineEquation>) was negatively associated. Frequent internet users(M = 7.6) and engaging with screens before bedtime (M = 7.7) had higher sleep health scores compared to non-frequent users (M = 6.90, <InlineEquation ID="IEq7"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq7.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p = 0.002\)</EquationSource> </InlineEquation>) and none or seldom engagement with screens before bedtime (M = 7.10, <InlineEquation ID="IEq8"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq8.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p= 0.003\)</EquationSource> </InlineEquation>) respectively. Linear regression showed TE positively associated (<InlineEquation ID="IEq9"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq9.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\beta\)</EquationSource> </InlineEquation> = 0.241, <InlineEquation ID="IEq10"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq10.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.012\)</EquationSource> </InlineEquation>) while TA negatively associated (<InlineEquation ID="IEq11"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq9.gif" Format="GIF" Height="17" Rendition="HTML" Resolution="72" Type="Linedraw" Width="15" /> </InlineMediaObject> <EquationSource Format="TEX">\(\beta\)</EquationSource> </InlineEquation> = -0.220, <InlineEquation ID="IEq12"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq12.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.029\)</EquationSource> </InlineEquation>) with sleep health. DSP was found to be a predictor of better satisfaction (OR: 1.32, <InlineEquation ID="IEq13"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq13.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p= 0.009\)</EquationSource> </InlineEquation>), efficiency (OR: 1.16, <InlineEquation ID="IEq14"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq14.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.026\)</EquationSource> </InlineEquation>), and duration of sleep (OR:1.16, <InlineEquation ID="IEq15"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq15.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p= 0.042\)</EquationSource> </InlineEquation>). Lower TA predicted better satisfaction (OR: 0.81, <InlineEquation ID="IEq16"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq16.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="62" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.04\)</EquationSource> </InlineEquation>), timing (OR: 0.74, <InlineEquation ID="IEq17"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq16.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="62" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.04\)</EquationSource> </InlineEquation>), and efficiency (OR:0.78, <InlineEquation ID="IEq18"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq18.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="62" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.01\)</EquationSource> </InlineEquation>) of sleep. Older adults who use technology one hour before sleep have better sleep timing (OR: 3.003, <InlineEquation ID="IEq19"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq19.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.002\)</EquationSource> </InlineEquation>), while those who do use mobile phones with a screen during the awake period after sleep onset have poor sleep timing (OR:0.016, <InlineEquation ID="IEq20"> <InlineMediaObject> <ImageObject Color="BlackWhite" FileRef="12889_2025_23894_Article_IEq19.gif" Format="GIF" Height="16" Rendition="HTML" Resolution="72" Type="Linedraw" Width="69" /> </InlineMediaObject> <EquationSource Format="TEX">\(p=0.002\)</EquationSource> </InlineEquation>). Conclusions DSP and TE support better sleep health, while TA negatively impacts sleep satisfaction, timing, and efficiency. Encouraging positive digital engagement and minimizing technology-related stress may promote healthier sleep in older adults.

Examining sleep health and its associations with technology use among older adults in Sweden: insights from a population-based study

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2025年8月22日

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Sarah Nauman Ghazi,
Anders Behrens,
Johan Sanmartin Berglund,
Jessica Berner,
Peter Anderberg

摘要

Introduction

Exploring the association between technology use and sleep health in older adults is important as digital engagement becomes integrated into society.

Objective

This study aimed to examine sleep health and its association with technology use in a population-based cohort of 60 years and older.

Methods

This cross-sectional, population-based study (2023) included 436 older adults from the Swedish National Study on Aging and Care, Blekinge (SNAC-B) population. These participants were sent questionnaires about their sleep, internet usage, Digital Social Participation (DSP), Technology Anxiety (TA), Technology Enthusiasm (TE), and use of information and communication technology. We used a multidimensional instrument, SATED, to measure sleep health. In this study, we conducted statistical analyses using the chi2 test, T-test, Pearson correlation, and backward linear and logistic regression.

Results

Our study found that older adults (60 years+) have a mean sleep health score of 7.40 (SD = 2.03). TE ( \(r = 0.18\) , \(p < 0.001\) ) and DSP ( \(r = 0.14\) , \(p = 0.004\) ) were positively associated with better sleep health, while TA ( \(r = -0.15\) , \(p = 0.003\) ) was negatively associated. Frequent internet users(M = 7.6) and engaging with screens before bedtime (M = 7.7) had higher sleep health scores compared to non-frequent users (M = 6.90, \(p = 0.002\) ) and none or seldom engagement with screens before bedtime (M = 7.10, \(p= 0.003\) ) respectively. Linear regression showed TE positively associated ( \(\beta\) = 0.241, \(p=0.012\) ) while TA negatively associated ( \(\beta\) = -0.220, \(p=0.029\) ) with sleep health. DSP was found to be a predictor of better satisfaction (OR: 1.32, \(p= 0.009\) ), efficiency (OR: 1.16, \(p=0.026\) ), and duration of sleep (OR:1.16, \(p= 0.042\) ). Lower TA predicted better satisfaction (OR: 0.81, \(p=0.04\) ), timing (OR: 0.74, \(p=0.04\) ), and efficiency (OR:0.78, \(p=0.01\) ) of sleep. Older adults who use technology one hour before sleep have better sleep timing (OR: 3.003, \(p=0.002\) ), while those who do use mobile phones with a screen during the awake period after sleep onset have poor sleep timing (OR:0.016, \(p=0.002\) ).

Conclusions

DSP and TE support better sleep health, while TA negatively impacts sleep satisfaction, timing, and efficiency. Encouraging positive digital engagement and minimizing technology-related stress may promote healthier sleep in older adults.