A Six-Year Longitudinal Cohort Study Assessing the Association Between Social Interaction Trajectories and Sleep Health in Older Adults

Summary

This 6-year longitudinal study uses a standardized index to show that declining social engagement is significantly associated with an increased risk of sleep deprivation and non-restorative sleep among community-dwelling older adults, highlighting the potential value of social interaction screening in healthy aging.

Abstract

Sleep disturbances, including sleep deprivation and non-restorative sleep, pose a severe public health burden for aging populations. While social isolation is a recognized risk factor, rigorous longitudinal studies evaluating the prospective association between dynamic changes in social interaction and subsequent sleep health are lacking. This study details a 6-year longitudinal cohort study involving 473 community-dwelling older adults in Japan who exhibited normal baseline sleep. Social interactions were systematically evaluated using the standardized 18-item Index of Social Interaction. Participants were tracked from 2017 to 2023 to categorize their social trajectories into distinct subgroups, such as persistently high, declining, improving, or persistently low. Sleep duration and restoration were concurrently assessed using national health guidelines. Multivariable logistic regression models were constructed to evaluate the longitudinal relationship of these social trajectories with sleep outcomes, adjusting for baseline demographic, physical, and lifestyle covariates. The study observed that individuals experiencing a longitudinal decline in social interaction, or those remaining persistently isolated, faced a more than two-fold increased risk of developing sleep deprivation and non-restorative sleep compared to those maintaining robust social networks. Conversely, an incremental increase in continuous social interaction scores was significantly associated with a reduced risk of adverse sleep outcomes. These longitudinal findings offer observational evidence regarding the psychosocial determinants of sleep. Integrating standardized social interaction metrics into routine community health surveillance may facilitate early, targeted interventions to support sleep health and promote healthy aging.

Introduction

In modern society, the lack of proper sleep has emerged as a critical public health problem, with the course of insomnia in older adults increasingly demonstrating a trend toward chronic development¹. A systematic evaluation has established that sleep disorders significantly elevate an individual’s risk of developing severe psychiatric conditions, including depression and accelerated cognitive decline². Recognizing the severity of this issue, the Ministry of Health, Labor and Welfare (MHLW) in Japan established the basic policy of Health Japan 21. Within this framework, the national health goals concerning sleep are explicitly defined as achieving “an increase in the number of people who are rested from sleep” and “an increase in the number of people who are getting enough sleep”³. Consequently, accurately measuring and defining what constitutes good sleep duration within community settings is paramount for public health surveillance.

According to the MHLW’s National Healthy Sleep Guidelines, the recommended sleep duration for older adults in Japan is set at a minimum of 6 h per day³. Chronic deviation from these guidelines yields severe physiological consequences. Epidemiological studies have demonstrated that elderly individuals who consistently experience curtailed sleep, particularly those sleeping for 5 h or less per night, exhibit significantly higher mortality rates⁴. Furthermore, consistently shortened sleep duration disrupts essential hormonal balances, including cortisol and serotonin regulation, which in turn exacerbates stress responses, impairs emotional regulation, and fosters heightened states of depression, anxiety, and lower overall life satisfaction^1,5.

While sleep duration provides a quantitative measure of sleep health, sleep restoration serves as a crucial subjective quality index, presumed to reflect true physiological sleep sufficiency. A critical, yet frequently under-assessed condition in aging cohorts is Non-Restorative Sleep (NRS). NRS describes a paradoxical state wherein an individual, despite achieving an adequate duration of sleep, continues to experience persistent fatigue or lacks a sense of restored energy upon waking⁶. Because older adults and the chronically ill are inherently more susceptible to the physiological and psychological impacts of poor sleep, NRS can silently precipitate a cascade of health complications⁷.

The psychological and cognitive toll of chronic NRS is heavily documented. Individuals suffering from NRS frequently exhibit symptoms of severe depression, anxiety, and irritability, as the lack of refreshing sleep fundamentally cripples emotional regulation⁸. Advanced analytical models, including latent profile analyses, have further associated NRS with an increased risk of psychosis-like experiences⁹ and identified it as a robust predictor of suicidal ideation in at-risk populations¹⁰. Cognitively, NRS hampers neuroplasticity and memory consolidation¹¹, leading to deficits in attention, concentration, and decision-making¹². In older demographics, persistent NRS is increasingly recognized as a potential risk factor that accelerates cognitive aging and contributes to neurodegenerative pathologies, such as Alzheimer’s and Parkinson’s disease¹³. Physiologically, the body’s failure to properly recover during sleep sustains prolonged systemic stress, contributing to inflammatory processes linked to cardiovascular diseases¹⁴, type 2 diabetes¹⁵, chronic pain¹⁶, and weakened immune function¹⁷.

Sleep health is governed by a complex matrix of biological, psychological, and social factors. Recently, social interaction has garnered significant attention as a primary determinant of both physical and mental well-being. Existing literature indicates that older adults suffering from diminished social networks or a reduced frequency of interpersonal interactions are highly susceptible to severe depression and anxiety¹⁸, as well as accelerated cognitive deterioration¹⁹. Conversely, active social relationships provide vital emotional support, cognitive engagement, and a structured daily routine, all of which act as protective mechanisms for overall health and psychological stability.

Despite this growing body of theoretical evidence regarding social isolation, a notable methodological gap persists in the literature. Most existing studies are limited by cross-sectional designs or single-timepoint assessments, which capture only a static snapshot of social engagement. Because social interaction is inherently dynamic and fluctuates with aging, retirement, and health transitions, single-timepoint methods inherently fail to evaluate long-term social trajectories or establish temporal sequence. There remains a distinct lack of rigorous, long-term prospective cohort studies capable of evaluating the longitudinal association between dynamic changes in social interactions and subsequent sleep status, particularly within the rapidly aging Japanese demographic. A longitudinal trajectory-based approach is therefore preferable and necessary, as it allows for the capture of cumulative social effects and shifting interpersonal patterns over time.

To address this critical gap, this study conducted a 6-year longitudinal analysis of community-dwelling older adults in Japan. By leveraging a validated 18-item Index of Social Interaction (ISI) alongside standardized MHLW sleep metrics, this study systematically tracked and visualized how distinct longitudinal trajectories of social interaction, categorized into subgroups representing persistent, declining, or improving social statuses, prospectively associate with the dual endpoints of sleep duration and sleep restoration. While this observational design cannot definitively establish causation, investigating these trajectory variables over an extended period yields valuable longitudinal evidence to inform and optimize targeted public health interventions for healthy aging. Readers should interpret the ISI trajectory categories as indicative trends of social behavior rather than absolute clinical diagnoses.

Protocol

This longitudinal cohort study was conducted in a suburban Japanese community as part of the Community Empowerment and Care (CEC) project and was approved by the Ethics Committee of the University of Tsukuba, Japan (Approval No. 1331-7). The research adhered to the principles of the Declaration of Helsinki, and written informed consent was obtained from all participants before study enrollment.

1. Participant recruitment and selection

A total of 473 community-dwelling older adults who completed the 6-year follow-up were enrolled based on predefined criteria. Participants were initially recruited via. mailed invitations distributed through local municipal registries. Eligibility was verified by trained research staff during an initial telephone screening or in-person interview, serving as the first validation checkpoint for cohort inclusion. Inclusion criteria comprised: age > 65 years; participation in the 2017 baseline survey; and presence of a normal sleep status at baseline, defined as sleeping ≥6 h per day and experiencing subjective sleep restoration. Exclusion criteria included: pre-existing sleep deprivation (<6 h of sleep per day) or non-restorative sleep (NRS) at baseline; complete loss of independent living ability; and missing core survey data or loss to follow-up during the 2017–2023 study period.

2. Baseline assessment and follow-up procedures

Participants completed standardized self-reported questionnaires at the 2017 baseline and the 2023 follow-up to collect demographic and lifestyle covariates. Data were primarily collected using paper-based, self-administered questionnaires. For participants requiring assistance, trained field investigators provided face-to-face standardized interviews to ensure data accuracy. To comply with data handling standards, all completed physical questionnaires were stored in secure, access-controlled filing cabinets at room temperature, while digitized data were encrypted and stored on password-protected institutional servers. Recorded data included age, sex, daily exercise habits, smoking status, alcohol intake, and subjective life satisfaction. Disease history was documented, specifically noting any hospitalization or treatment lasting more than 2 weeks in the past year. Furthermore, the participants' baseline nutritional and motor function levels were assessed using the validated subscale from the Ministry of Health, Labor and Welfare's Kihon Checklist. Follow-up evaluations were conducted 6 years post-baseline to reassess all core metrics and track longitudinal changes. The 2023 follow-up utilized the identical administration protocols, questionnaire formats, and data entry validation checks as the baseline to ensure strict longitudinal consistency.

3. Outcome definitions and social interaction categorization

The primary study endpoints were sleep duration and sleep restoration at the 6-year follow-up. Sleep duration was assessed using the specific prompt: “On average, how many hours of actual sleep do you get in 24 h?” Responses were recorded numerically and subsequently dichotomized, with insufficient sleep defined as <6 h per day. Sleep restoration was evaluated utilizing the binary (Yes/No) prompt derived from national guidelines: “Do you feel you get adequate rest from your sleep?” with a negative response indicative of NRS.

Social interactions were evaluated utilizing the 18-item Index of Social Interaction (ISI). Each of the 18 items was scored as 1 (indicative of positive social engagement) or 0 (indicative of negative or absent engagement). Total scores were computationally calculated by summing the item responses, yielding a potential range of 0 to 18. To quantify social trajectories, the continuous change in ISI score was calculated by subtracting the baseline score from the 2023 follow-up score. Additionally, participants were stratified into “high” (ISI ≥ 16) and “low” (ISI < 16) subgroups based on the cohort-derived median score calculated specifically from this study's baseline dataset (Median = 16). This enabled the construction of four distinct social interaction trajectory groups (low-to-low, high-to-low, low-to-high, and high-to-high) to monitor dynamic shifts over the 2017–2023 interval.

4. Statistical analysis workflow

Data analysis utilized SPSS software (version 26.0). Participants with missing data for core variables were excluded from the analytical sample using listwise deletion before modeling. Descriptive statistics were employed to summarize the demographic characteristics of the final analytical sample. Analytical approaches encompassed group comparisons using Chi-square tests for categorical variables, and Mann-Whitney U tests for continuous variables with skewed distributions.

To examine the associations between changes in social interaction and sleep outcomes, multivariable logistic regression models were constructed. Covariates included in these models were selected based on a priori clinical relevance and preliminary bivariate screening, retaining variables that demonstrated statistical significance (p. < 0.05) in unadjusted analyses. Before logistic regression, the assumption of no severe multicollinearity among independent variables was verified using Variance Inflation Factors (VIF < 5).

Operationally, the analyses were executed in SPSS using the “Analyze > Regression > Binary Logistic” workflow. The “Enter” method was specified to simultaneously include all selected covariates (e.g., age, exercise, life satisfaction) alongside the primary ISI predictors into the models (Addresses 5e). These models evaluated continuous changes, categorized changes, and median subgroup trends. Statistical significance was established at p. < 0.05 (two-tailed).

Results

Participant characteristics and study flow
The participant selection process for this 6-year longitudinal study is detailed in Figure 1. Of the initial 2,350 community-dwelling older adults invited to the 2017 baseline survey, 1,188 were excluded due to missing core data, loss of independent living, or pre-existing sleep issues (sleep deprivation or non-restorative sleep). This resulted in a baseline cohort of 1,162 individuals with normal sleep status. During the 6-year...

Discussion

Through a standardized 6-year longitudinal assessment, this study demonstrated that dynamic changes in the social interaction trajectories of older adults are prospectively associated with their subsequent sleep status. The results indicate that maintaining or improving social interactions is associated with a reduced risk of sleep deprivation and non-restorative sleep (NRS). Conversely, a reduction in social activities or prolonged social isolation more than doubles the risk of adverse sleep outcomes within this observa...

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
IBM SPSS Statistics (Version 26.0)	IBM Corp.	RRID: SCR_019096
Index of Social Interaction (ISI) Questionnaire	Developed by Anme et al.	Anme, T.,et al.31
Kihon Checklist	Ministry of Health, Labour and Welfare, Japan	Satake, S. et al.30
Microsoft Excel	Microsoft Corporation	RRID: SCR_016137