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Learn more: PMC Disclaimer | PMC Copyright Notice Age Ageing . 2026 Apr 12;55(4):afag076. doi: 10.1093/ageing/afag076 Search in PMC Search in PubMed View in NLM Catalog Add to search Mental health in the peri-hospital period of older patients: the roles of physical symptoms burden and sleep quality Juliana Smichenko Juliana Smichenko 1 University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel 2 Clalit Research Institute, Innovation Division, Clalit Health Services, Tel-Aviv, Tel-Aviv District, 6209804, Israel 3 University of Haifa - The Center of Research and Study of Aging, Haifa, Haifa District, 3498838, Israel Find articles by Juliana Smichenko 1, 2, 3, ✉ , Anna Zisberg Anna Zisberg 4 University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel 5 University of Haifa - The Center of Research and Study of Aging, Haifa, Haifa District, 3498838, Israel 6 University of Haifa - ADVANCE Azrieli Advanced Nursing Center, Haifa, Haifa District, 3498838, Israel Find articles by Anna Zisberg 4, 5, 6 , Tamar Shochat Tamar Shochat 7 University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel Find articles by Tamar Shochat 7 Author information Article notes Copyright and License information 1 University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel 2 Clalit Research Institute, Innovation Division, Clalit Health Services, Tel-Aviv, Tel-Aviv District, 6209804, Israel 3 University of Haifa - The Center of Research and Study of Aging, Haifa, Haifa District, 3498838, Israel 4 University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel 5 University of Haifa - The Center of Research and Study of Aging, Haifa, Haifa District, 3498838, Israel 6 University of Haifa - ADVANCE Azrieli Advanced Nursing Center, Haifa, Haifa District, 3498838, Israel 7 University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel ✉ Address correspondence to: Juliana Smichenko, University of Haifa - Faculty of Social Welfare and Health Sciences,The Cheryl Spencer Department of Nursing, Haifa, Haifa District, Israel. Email: [email protected] , [email protected] Received 2025 Jul 20; Revised 2026 Feb 4; Accepted 2026 Feb 11; Collection date 2026 Apr. © The Author(s) 2026. Published by Oxford University Press on behalf of the British Geriatrics Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License ( https://creativecommons.org/licenses/by-nc/4.0/ ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected] for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact [email protected]. PMC Copyright notice PMCID: PMC13071403 PMID: 41967034 Abstract Background Anxiety and depressive mood are common among older adults at the peri-hospital period, impacting independence and well-being. Hospital factors such as physical symptoms burden (PSB) and sleep quality (SQ), may impact mood trajectories. This study aims to explore changes in anxiety and depressive mood from admission to discharge and one-month post-discharge, assessing the role of SQ, PSB and sedative-hypnotic medications (SHM) burden across timepoints. Methods This is a prospective study of older patients (65+) hospitalized in acute medical units. Interviews were conducted at admission, discharge and one-month post-discharge. A two-stage modelling approach was applied, separating in-hospital and peri-hospital trajectories of anxiety and depressive mood. Mixed-models analyses examined contributions of SQ and PSB, controlling for SHM burden, demographic and baseline characteristics. Results Of 683 (male: 54.9%; age 77.31 ± 6.67), 643 completed discharge interviews and 545 completed one-month post-discharge interviews. Anxiety and depressive mood worsened over time. Poor SQ, greater PSB (B anx = .39, P < .001; B dep = .25, P = < .001), and higher SHM burden (B anx = .55, P < .01) significantly predicted worse mental outcomes throughout hospitalization and post-discharge. SQ effects varied by timepoints: poor SQ was associated with heightened anxiety post-discharge (F (2,878) = 7.52, P < .001) and worsened depressive mood during hospitalization (F (2,874) = 7.99, P < .001). Higher SHM burden correlated with depressive mood at discharge (F (2,874) = 4.80, P < .01). Conclusion Findings highlight the need to address anxiety and depressive mood in hospitalized older adults through improved sleep, symptoms relief, and cautious SHM use. Further research should explore long-term interventions to support mental health during and after hospitalization. Keywords: depressive mood, anxiety, medication use, older people Key Points Medically ill older patients experience anxiety and depressive mood alternations around acute hospitalization. Longitudinal analysis revealed that better sleep quality was associated with lower post-hospitalization anxiety while lower sleep quality was associated with higher depressive mood during hospital stay. Physical symptoms and sedative-hypnotic medications burdens were associated with poorer mental outcomes around hospitalization; higher sedative burden was associated with higher depressive mood at discharge. Background Anxiety and depressive mood fluctuations are common among older adults peri-hospitalization, impacting independence and well-being [ 1 , 2 ]. Up to 60% of older inpatients report depressive symptoms, yet their trajectories vary across timepoints [ 1 , 3 , 4 ]. A study found that 40% of older inpatients had persistent cognitive-affective depressive symptoms from hospitalization through three months post-discharge, leading to functional decline, falls, and increased mortality [ 1 ]. Another study reported high depressive symptoms at admission in 37% of older inpatients, with 8% remaining highly depressed and 19% developing new symptoms by three months post-discharge [ 4 ]. Anxiety is also prevalent, affecting up to 60% of older inpatients [ 5 , 6 ]. A study following geriatric medical inpatients over a year post-discharge found a steady increase in anxiety [ 2 ], while another following trauma patients for seven months identified two patterns: one with increasing anxiety in- and post-hospitalization (12.7%) and another with declining anxiety (87.3%) [ 7 ]. However, some studies suggest anxiety is highest at admission and declines over time [ 8 ]. Research on anxiety and depression (or related situational conditions such as depressive symptoms and depressed mood) trajectories around hospitalization remains scarce; Many studies suffer from methodological limitations such as overlapping terminology (depression/depressive symptoms/depressed mood), cross-sectional designs, key hospitalization timepoints exclusion (e.g. admission, discharge), or mixed hospital settings not focusing solely on internal medicine older patients [ 5–7 , 9 , 10 ]. Addressing anxiety and depression peri-hospitalization is essential due to their potential impact on adverse outcomes, including falls, frailty, and mortality [ 1 , 6 , 11 ]. Further research in older adults hospitalized for acute and chronic medical conditions is needed, particularly with longitudinal designs. Physical symptoms burden (PSB) is a major concern during the peri-hospitalization period, often leading individuals to seek medical care for relief [ 12 ]. Research links higher symptom burden to both minor and major depression [ 13 ], with most studies focusing on community and cancer populations rather than hospitalized older adults [ 14 , 15 ]. Other studies focus on associations between specific symptoms, such as dyspnea and pain, with mood disorders during hospitalization [ 16 ], while others examine overall symptom burden and mood in hospitalized cancer patients [ 15 ]. However, most hospital-based research about symptoms burden and mood disorders remains descriptive and cross-sectional, with limited focus on medically-ill older adults [ 17–19 ]. Understanding the impact of symptoms on various physiological and psychological outcomes beyond physical discomfort is crucial, as symptoms can influence key health indicators such as sleep, overall well-being, and even survival [ 14 ]. Sleep disturbances are common in hospitalization, affecting nearly half of adult and older patients [ 20 , 21 ]. They are linked to adverse clinical, cognitive, and functional outcomes [ 20 , 22 ] and share bidirectional relationships with depression and anxiety [ 23 ]. Once considered symptoms of these conditions, sleep disturbances are now recognized as potential contributors to their development and exacerbation [ 14 , 24 ]. While hospitalized older patients have primarily been studied for depression and anxiety as predictors of sleep disturbances [ 25 ], little is known about how changes in sleep patterns during hospitalization impact mental health trajectories. Changes in sedative-hypnotic medication (SHM) burden may induce neurochemical instability via GABAergic and monoaminergic pathways, with studies showing acute mental side effects of short-term exposure or disruption [ 26–28 ]. While SHM changes are linked to delirium and cognitive decline [ 29 , 30 ], evidence on acute mental outcomes remains limited, especially in older adults. Studying these predictors together aligns with person-centered frameworks such as ‘ what matters to the patient ’ [ 31 ], which places patients’ priorities and experiences regarding their personal treatment goals at the center of clinical practice. However, these highly prevalent factors have rarely been assessed jointly in older-adults peri-hospitalization. This study aimed to explore anxiety and depressive mood trajectories from admission to discharge and one-month post-discharge; to assess the impact of key intervening factors, including PSB and sleep quality (SQ), using a repeated-measures mixed models design. We hypothesized that (a) greater PSB would be associated with worsened anxiety and depressive mood during hospitalization, and (b) poorer SQ trajectory would correlate with worsening anxiety and depressive mood peri-hospitalization. Methods Study design This study is an observational, prospective multi-center study conducted in four Israeli hospitals, between 2018 and 2021: the ‘Hospitalization Process Effects on Mobilization Outcomes and Recovery' (HoPE-MOR). HoPE-MOR investigated the relationship between inpatient care processes and functional outcomes in older adults from admission through hospitalization in internal medicine units, with follow-up extending to one-month post-discharge [ 21 ]. Participants The study included 683 older participants (≥65 years) with preserved cognitive function, a completed admission interview, and at least one follow-up during hospitalization. Of them, 643 completed the discharge interview, and 545 participated in the one-month post discharge telephone interview (see Figure 1 ). Inclusion and exclusion criteria are described previously [ 21 ]. Additional study information is available at the Australian New Zealand Clinical Trials Registry (ANZCTR: ACTRN12618001761257). Figure 1. Open in a new tab Flowchart of included participants and outcomes availability across timepoints. a HADS = Hospital Anxiety and Depression Scale. Outcome measures Anxiety and depressive mood were assessed using the Hospital Anxiety and Depression Scale (HADS) [ 32 ] a validated tool with reliability scores of 0.80 for anxiety and 0.81 for depression [ 33 , 34 ]. The tool has been internationally translated and officially tested, including in Hebrew, Russian, and Arabic. The HADS was administered at admission, discharge, and one-month post-discharge. It consists of 14 questions, rated on a 4-point scale. Anxiety and depressive mood were analysed both as continuous variables and categorized into normal/borderline/abnormal levels at admission for control analyses. Predictors Sleep Quality (SQ) was assessed via a self-reported sleep diary asking ‘How would you rate your overall sleep?’ on a 4-category scale: ‘very bad’, ‘pretty bad’, ‘pretty good’, and ‘very good’ [ 21 ]. Data were collected at admission (reflecting pre-hospitalization SQ), daily during hospitalization, and at one-month post-discharge. Physical symptoms burden was assessed using the modified Patient Health Questionnaire (PHQ) [ 18 , 21 , 35 ]. Participants rated symptoms intensity on a scale of 1 to 10, with 0 indicating the absence of a symptom. Symptoms covered pain, fatigue, vertigo, cough, gases, nausea, diarrhoea, constipation, palpitations, and dyspnea. Daily scores were averaged for overall symptom burden. Sedative-hypnotic medication (SHM) burden was quantified using the Drug Burden Index (DBI), a validated measure assessing sedative-hypnotic and anticholinergic medications impact on cognitive and physical function [ 36 , 37 ]. DBI scores range from 0 to 1 based on daily medication and minimum effective dose. SHM burden was assessed at admission, during hospitalization (average daily burdens), and one-month post-discharge. The DBI calculation method is detailed in previous research [ 21 , 37 ]. Covariates Cognitive function on admission was assessed using the Adult Lifestyle and Function Interview–Mini-Mental State Examination (ALFI-MMSE) [ 38 ], which served as an inclusion criterion for determining patients’ eligibility for participation. Daily, patients were monitored for acute cognitive decline. Functional status was evaluated via the Barthel Index for Activities of Daily Living (BADL) [ 39 ]. Comorbidity burden was assessed using Charlson’s Comorbidity Index (CCI) [ 40 ], and severity of acute illness was assessed using the New Early Warning Scale (NEWS) [ 41 ]. Additional covariates included age, gender, hospital length of stay. Data collection Study recruitment occurred between February 2018 and July 2021. Nursing staff provided daily lists of eligible patients, and trained researchers conducted in-person interviews at admission, daily (up to 7 hospital days), discharge, and one-month post-discharge via telephone. Additional data about medication use and healthcare condition were collected through electronic health records. Additional details of data collection is addressed elsewhere [ 21 ]. Data analysis Anxiety, depressive mood, PSB, and SHM burden were analysed as continuous variables (PSB and SHM were calculated as mean at discharge); SQ was treated ordinally, averaged per time-point, with in-hospital follow-ups summarized as the discharge sleep variable. Descriptive statistics included means (SD) and frequencies (percentages). Pearson and Spearman correlations assessed associations between variables ( P < .05). Baseline characteristics were analysed for three groups: (i) all participants at admission, (ii) those completing discharge interviews, and (iii) those completing one-month post-discharge interviews ( Table 1 ). Additional analyses compared post-discharge HADS completers to non-completers. Completers were younger, more independent, less acutely ill, and had shorter hospital stays. Analyses were conducted using SPSS version 27.0 (IBM Corp., Armonk, NY). Table 1. Baseline characteristics of available study participants on admission, discharge and one-month post discharge Total sample at admission (N = 683) Total sample with b HADS at discharge interview (N = 623) Total sample with HADS at one-month post-discharge interview (N = 502) Age a [M ± SD] years: range [65–96] 77.3 ± 6.7 77.3 ± 6.7 76.9 ± 6.6 Gender [n(%)] male 375 (54.9) 345 (55.4) 272 (54.2) Length of stay [M ± SD] days 6.1 ± 5.2 5.9 ± 5.1 5.6 ± 4.8 Cognitive function at admission ( c ALFI-MMSE) (0–22) [M ± SD] 18.5 ± 3.2 18.6 ± 3.2 18.6 ± 3.0 Comorbidities ( d CCI) [M ± SD] 2.3 ± 2.1 2.3 ± 2.1 2.2 ± 2.0 Acute of illness ( e NEWS) at least one parameter above 3 [n(%)] 202 (29.6) 179 (28.7) 135 (26.9) Functional performance (ADL) Barthel Index at admission (0–100) [M ± SD] 89.5 ± 18.1 90.1 ± 17.6 90.6 ± 17.4 Depressive mood (HADS) at admission As continuous [M ± SD]: By categories [n(%)]: Normal Borderline Abnormal 5.7 ± 4.4 456 (67.6) 112 (16.6) 107 (15.9) 6.1 ± 4.6 430 (69.4) 101 (16.2) 89 (14.4) 7.1 ± 4.9 347 (69.3) 79 (15.7) 75 (15.3) Anxiety (HADS) at admission As continuous: [M ± SD] By categories [n(%)]: Normal Borderline Abnormal 4.5 (4.0) 545 (80.6) 68 (10.1) 63 (9.3) 4.32 ± 3.9 508 (81.9) 62 (10.0) 50 (8.1) 4.6 ± 4.1 399 (79.6) 51 (10.2) 51 (10.2) Open in a new tab Values are presented as N (%) or M ± SD. a M ± SD = Mean ± Standard Deviation; b HADS = Hospital Anxiety and Depression Scale; c ALFI-MMSE = Adult Lifestyles and Function Interview Mini Mental State examination d CCI = Charlson’s Comorbidity Index; d NEWS = National Early Worning Scale; e ADL = Activities of Daily Living. Number of observations varies between variables due to missing data. A two-stage modelling approach assessed anxiety and depressive mood trajectories: ‘Stage-1’ short-term hospitalization trajectory , examining changes from admission to discharge and reporting PSB before and during hospitalization; and ‘Stage-2’ extended peri-hospitalization trajectory , evaluating changes across admission, discharge, and one-month post-discharge while controlling for admission PSB. Repeated measures mixed models were used in both stages, adjusting for age, gender, length of hospital stay, cognitive and functional status, illness severity, comorbidity, baseline mood, SHM burden, and SQ. Collinearity was assessed for each of the variables (for the full details of sensitivity analyses see Appendix 1 in the Supplementary Data Section). Sensitivity analyses were performed for COVID-19 pandemic period, for participants with all 3 timepoints (excluding acute cognitive decline) and participants with ‘normal’ anxiety/depressive mood at admission (for the full details of sensitivity analyses see Appendices 2 , 3 , and 4 in the Supplementary Data Section). All models were performed using SAS version 9.4 (SAS® institute Inc., Cary, NC). Ethical considerations This study was approved by both University of Haifa ethics committee (No. 324/17) and the local Helsinki committees of four medical centres (0100–15-EMC, 0099–18-BNZ, 0166–18-CMC, 0263–19-RMB). Results Participants baseline characteristics This study included 683 participants who had completed admission and at least one follow-up, with 643 completed discharge and 545 completing a one-month post-discharge telephone interview. Three-timepoints (admission, discharge, and post-discharge) were analysed in longitudinal models ( Figure 1 ). Participants were predominantly male (54.9%), aged 77.3 ± 6.6, with relatively high self-reported physical function (89.5 ± 18.1). Nearly one-third of participants needed intensive supervision due to illness acuity. Notably, demographic and clinical characteristics, including age, gender, length of stay, cognitive function, and comorbidities, remain relatively stable across the groups (see table 1 ). Anxiety (borderline/abnormal) was present in 19.4% and depressive mood in 32.5% of participants at admission. Average scores of SQ were: 1.7 ± 0.9, 1.6 ± 0.8, and 1.7 ± 0.8 at admission, discharge and post-discharge, respectively. PSB scores were 2.9 ± 2.0 at admission and 2.0 ± 1.7 at discharge. Average scores of SHM burden were: 0.5 ± 0.7, 0.4 ± 0.5, and 0.5 ± 0.7 at admission, discharge and post-discharge, respectively ( fig. 2A-D ). Figure 2. Open in a new tab Trajectories of main study outcomes and variables. Trajectories of main outcomes, and independent variables: A. anxiety and depressive mood, B. sleep quality, C. physical symptoms burden and D. sedative-hypnotic medications burden at the peri-hospital period Two-stage modelling analysis RM mixed-models analysed anxiety and depressive mood changes over time, focusing on admission-to-discharge changes and admission-discharge-post-discharge trajectories (see Tables 2 – 3 ). Table 2. Repeated measures mixed models for mental outcomes at time of discharge –stage 1 model Stage-1 model Admission-discharge period Anxiety Depressive mood Variables B estimate 95% CI F / t statistic B estimate 95% CI F / t statistic Intercept 12.04 [8.42–15.69] t (621) = 6.52 *** 13.22 [9.01–17.43] t (623) = 6.17 *** Timepoints 0.16 [−0. 15–0.48] F (1,554) = 0.93 0.96 [0.61–1.31] F (1,550) = 28.38 *** Gender (male) −0.72 [−1.20–−0.26 F (1,554) = 9.26 ** 0.41 [−0.13–0.96] F (1,550) = 2.22 Age; years −0.04 [−0.08–−0.00] F (1,554) = 5.99 * 0.03 [−0.01–0.07] F (1,550) = 2.50 Length of stay in medical unit (days) 0.02 [−0.02–0.06] F (1,554) = 0.93 0.01 [−0.04–0.06] F (1,550) = 0.30 Acuity of illness ( a NEWS) −0.32 [−0.82–0.18] F (1,554) = 1.60 0.39 [−0.18–0.98] F (1,550) = 1.80 Comorbidities ( b CCI) −0.09 [−0.20–−0.02] F (1,554) = 2.53 0.12 [−0.00–0.25] F (1,550) = 3.57 Functional performance ( c ADL) −0.03 [−0.04–−0.011] F (1,554) = 11.99 *** −0.05 [−0.07–−0.03] F (1,550) = 32.65 *** Symptom burden at admission-discharge 0.47 [0.35–0.59] F (1,554) = 60.31 *** 0.42 [0.29–0.56] F (1,550) = 35.69 *** Depressive mood at admission: Normal Borderline abnormal −2.77 [−3.45–−2.09] −0.47 [−1.28–0.33] Ref [0] F (2,554) = 46.65 *** N/A N/A Anxiety at admission: Normal Borderline abnormal N/A N/A −3.60 [−4.58–−2.62] −1.70 [−2.90–−0.50] Ref [0] F (1,550) = 30.62 *** Cognitive performance at admission ( d ALFI-MMSE) −0.03 [−0.10–0.05] F (1,554) = 0.43 −0.22 [−0.31–−0.13] F (1,550) = 24.05 *** SHM burden 0.51 [0.16–0.86] F (1,554) = 8.26 ** 0.44 [0.04–0.85] F (1,550) = 4.66 * Sleep Quality −0.35 [−0.57–−0.12] F (1,554) = 8. 83 ** −0.33 [−0.59–−0.07] F (1,550) = 6.20 ** Open in a new tab a NEWS=National Early Worning Scale; b CCI=Charlson’s Comorbidity Index; c ADL = Activities of Daily Living; d ALFI-MMSE = Adult Lifestyles and Function Interview Mini Mental State examination; Ref = reference; N/A = not available (not included in analysis). Analysis of mixed models: (n = 635) Subjects; maximum 2 observations per subject; anxiety observations - 1189; depressive mood observations - 1187. * P < .05; ** P < .01; *** P < .001. Estimates represent marginal population-average fixed effects from the mixed models and do not reflect within-subjects change scores. Timepoints are referred to admission (0) and discharge (1). Table 3. Repeated measures mixed models for mental outcomes up to one-month post discharge– Stage 2 model Stage-2 model Admission-discharge-one month post discharge period Anxiety Depressive mood Variables B estimate 95% CI F / t statistic B estimate 95% CI F / t statistic Intercept 12.94 [9.39–16.50] t (623) = 5.79 *** 13.55 [9.51–17.59] t (622) = 6.58 *** Timepoints: —admission —discharge —post-discharge Ref [0] −0.74 [−1.51–0.03] 1.59 [0.63–2.54] F (2,878) = 10.98 *** Ref [0] 0.33 [−0.60–1.26] 3.41 [2.28–4.5] F (2,874) = 19.22 *** Gender (male) −0.71 [−1.17–−0.25] F (1,878) = 9.33 ** 0.34 [−0.18–0.86] F (1,874) = 1.67 Age; years −0.04 [−0.07–−0.00] F (1,878) = 5.39 * 0.04 [0.00–0.08] F (1,874) = 5.14 * Length of stay in medical unit (days) 0.02 [−0.02–0.06] F (1,878) = 0.85 0.005 [−0.04–0.05] F (1,874) = 0.05 Acuity of illness ( a NEWS) −0.33 [−0.81–0.15] F (1,878) = 1.82 −0.31 [−0.86–0.24] F (1,874) = 1.23 Comorbidities ( b CCI) −0.13 [−0.24–−0.02] F (1,878) = 5.81 * 0.10 [−0.02–0.22] F (1,874) = 2.57 Functional performance ( c ADL) −0.03 [−0.05–−0.02] F (1,878) = 20.30 *** −0.05 [−0.07–−0.04] F (1,874) = 45.69 *** Physical Symptom burden at admission 0.39 [0.26–0.51] F (1,878) = 39.46 *** 0.25 [0.11–0.38] F (1,874) = 12.37 *** Depressive mood at admission: Normal Borderline abnormal −2.38 [−3.04–−1.72] −0.23 [−1.01–0.54] Ref [0] F (2,878) = 39.22 *** N/A N/A Anxiety at admission: Normal Borderline abnormal N/A N/A −3.37 [−4.30–−2.43] −1.50 [−2.64–−0.37] Ref [0] F (2,874) = 30.13 *** Cognitive performance at admission ( d ALFI-MMSE) −0.04 [−0.12–0.04] F (1,878) = 1.08 −0.21 [−0.29–−0.12] F (1,874) = 23.29 *** e SHM burden 0.55 [0.22–0.87] F (1,878) = 10.63 ** 0.36 [−0.09–0.8] F (1,874) = 5.10 * SHM burden * timepoints SHM * admission SHM * Discharge SHM * post discharge N/A N/A Ref [0] 0.69 [0.06–1.32] −0.43 [−1.07–0.21] F (2,874) = 4.80 ** Sleep Quality −0.52 [−0.83–−0.21] F (1,878) = 35.38 *** −0.38 [−0.73–−0.04] F (1,874) = 35.09 *** f SQ * timepoints SQ * admission SQ * Discharge SQ * post discharge Ref [0] 0.27 [−0.15–0.70] −0.75 [−1.25–−0.25] F (2,878) = 7.52 *** Ref [0] −0.05 [−0.52–0.42] −1.08 [−1.64–−0.52] F (2,874) = 7.99 *** Open in a new tab a NEWS=National Early Worning Scale; b CCI=Charlson’s Comorbidity Index; c ADL = Activities of Daily Living; d ALFI-MMSE = Adult Lifestyles and Function Interview Mini Mental State examination; e SHM = sedative-hypnotic medications; f SQ = sleep quality; N/A = not available (not included in analysis). Analysis of mixed models: (n = 635) Subjects; maximum 3 observations per subject; anxiety observations - 1518; depressive mood observations - 1515. Ref = Reference; * P < .05; ** < .01; *** < .001. Estimates represent marginal population-average fixed effects from the mixed models and do not reflect within-person change scores. Given the significant time interactions, the main effect of SHM burden (for depressive mood) and sleep quality (for anxiety and depressive mood) reflect their association at baseline only; therefore, the direction and confidence interval of these coefficients are not interpretable as an overall effect despite statistical significance. Stage 1: mental health during hospitalization Anxiety The RM mixed-models analysis for anxiety revealed several statistically significant main effects. Increased physical symptoms severity (B = .47, P < .001), higher SHM burden (B = .51, P < 0.01), and younger age (B = −.04, P = .015) were all associated with greater anxiety. In contrast, better SQ (B = −.35, P < .01) and higher functional performance (B = −.02, P < .001) were linked to lower anxiety. Depressive mood at admission significantly predicted higher anxiety, with greater severity associated with increased anxiety ( F (2,554) = 46.65, P < .001). Additionally, males reported significantly lower anxiety scores than females (B = −.73, P = < .01). No significant effects were found for timepoints (admission to discharge), comorbidities, length of stay, or cognitive function. No interactions were observed in this model. Depressive mood The RM mixed-models analysis revealed significant main effects for depressive mood. Depressive mood worsened from admission to discharge (B = .96, P = < .001). Higher PSB (B = .42, P < .001), SHM burden (B = .44, P < .01), and lower SQ (B = −.33, P = .01) were linked to increased depressive mood. In contrast, higher functional performance (B = −.05, P < .001) and cognitive function (B = −.22, P < .001) were associated with lower depressive mood scores. Anxiety at admission significantly predicted higher depressive mood scores, with greater severity linked to worse depressive mood ( F (2,550) = 30.62, P < .001). Gender, age, acute illness severity, comorbidities, and length of stay had no significant effects, and no interactions were found. Stage 2: comparison of mental health in peri-hospital period Anxiety trajectory Several significant main effects were found for anxiety. Worse PSB (B = .39, P < .001) and higher SHM burden (B = .55, P < .01) were associated with increased anxiety, while better functional performance (B = −.03, P < .001), greater comorbidity burden (B = −.13, P = .016), and older age (B = −.04, P = .02) were linked to lower anxiety. Depressive mood at admission significantly predicted higher anxiety ( F (2,878) = 39.22, P < .001). Gender also had a significant effect, with males reporting lower anxiety than females (B = −.70, P < .01). No significant effects were found for cognitive function, length of stay, or acute illness severity. A significant interaction between timepoints and SQ on anxiety ( F (2,878) = 7.52, P < .001) showed varying associations across timepoints. Simple slopes analysis revealed that higher SQ was more strongly linked to reduced anxiety one-month post-discharge compared to discharge (slope difference [SE] = 1.02 (0.26), P < .001) and admission (slope difference [SE] = 0.75 (0.25), P < .01). Depressive mood trajectory The RM mixed models revealed several significant main effects: higher age (B = .043 , P = .023) and greater PSB at admission (B = 0.25, P < .001) were associated with worsened depressive mood; better functional (B = −.05, P < .001) and cognitive status (B = −.21, P < .001) were linked to lower depressive mood scores. Borderline and abnormal anxiety at admission significantly increased depressive mood ( F (2,874) = 30.13, P < .001), with differences from the normal group (B = −3.37, P < .001; B = −1.50, P < .001). Gender, severity of acute illness, comorbidity burden, or length of stay were not significant. Two significant interactions were found: (i) between SQ and timepoints on depressive mood ( F (2,874) = 7.99, P < .001), linked to worse depressive mood at admission (slope difference [SE] = 1.08 (0.28), P < .001) and discharge (slope difference [SE] = 1.03 (0.29), P < .001) compared to one-month post discharge, with no significant difference between admission and discharge. (ii) Interaction between SHM burden and timepoints on depressive mood ( F (2,874) = 4.80, P < .01), showing SHM burden strongly positively associated with worsened depressive mood at discharge (Estimate [SE] = 1.05 (0.37), P < .001) compared to other timepoints. This association remained significant between discharge and one-month post-discharge (slope difference [SE] = 1.12 (0.37), P < .01), but not between admission and one-month post-discharge. Discussion This study aimed to explore anxiety and depressive mood trajectories in older adults during hospitalization and the peri-hospital period, using a two-phase longitudinal mixed-design approach. The first phase focused on the in-hospital period, examining how acute illness, symptom burden, and SQ associated with anxiety and depressive mood. The second phase extended up to one-month post-discharge, analysing changes in mental health while considering demographic and clinical factors, including SQ and medication burden longitudinal effect. Trajectories of mental health Hospitalization distinctly affected anxiety and depressive mood trajectories. While anxiety remained stable, with a minor, non-significant decrease from admission to discharge, likely due to ongoing physical symptoms and the stressful hospital environment [ 8 , 17 , 42 ]; depressive mood worsened progressively, consistent with literature showing that hospitalized older adults are vulnerable to increased emotional distress [ 9 , 43 ]. Depressive mood was more strongly linked to cognitive status, highlighting anassociation between lower cognitive performance and higher emotional distress. One-month post-discharge emerged as a critical period for worsening anxiety and depressive mood, surpassing admission levels, mirroring previous studies [ 1 , 2 , 4 , 7 ]. Our study adds by integrating in-hospital factors like SQ and SHM burden to explore their impact on mental health across the peri-hospital period. PSB and mental health PSB was a significant factor associated with both anxiety and depressive mood, supporting our hypothesis that increasing symptom burden contributes to psychological distress during hospitalization. This finding builds on prior research focused on specific symptoms, such as dyspnea and pain, showing that the changing patterns of symptom burden play a critical role in shaping mental health outcomes during and after hospitalization [ 16 , 17 , 44 ]. Symptoms are not only the primary reason for seeking medical care but also a key determinant of well-being, making their management crucial. Our findings demonstrated that symptoms were often unresolved by discharge, potentially prolonging emotional distress post-hospitalization [ 45 ]. The continuing symptoms burden may be assumed to exacerbate anxiety and depressive mood by creating uncertainty about health status and recovery, which reinforces feelings of helplessness. SQ and mental health The interactions between SQ and timepoints indicate shifting effects on mental health. Poor SQ was strongly associated with worsened depressive mood during hospitalization, supporting the bidirectional relationship between sleep disturbances and depression in older adults [ 17 , 46 ]. Hospitalization often disrupts sleep due to medical procedures, discomfort, and an unfamiliar environment, contributing to mood deterioration during this period [ 42 ]. However, by one month post-discharge, the association between SQ and depressive mood weakened, suggesting that other stressors, such as social isolation, difficulty managing medical conditions, and functional decline, may have become more influential in driving depression after hospitalization [ 9 ]. In contrast, the relationship between SQ and anxiety followed a different trajectory. While anxiety remained stable during hospitalization, it increased sharply after discharge and was strongly associated with SQ: improved SQ was associated with reduced anxiety one-month post-discharge compared to hospital period. This shift can be attributed to the fact that, during hospitalization, anxiety is likely driven by acute illness, medical interventions, and hospital stressors, overshadowing the impact of sleep disturbances [ 46 ]. After discharge, when patients must manage recovery independently, SQ plays a larger role in regulating anxiety. Improved SQ may contribute to better physical function, emotional resilience, and stress regulation, potentially mitigating the rise in anxiety after discharge [ 20 , 22 , 47 ], although causal mechanisms cannot be inferred from the present design. SHM burden and mental health trajectories SHM burden appeared to be a consistent correlate of anxiety and depressive mood higher sedative-hypnotic medication (SHM) burden was associated with increased anxiety and depressive mood across the peri-hospital period, with a time-dependent effect on depressive mood. Although SHM burden decreased slightly during hospitalization, it was still strongly linked to worsened depressive mood at discharge. This supports research showing a dose-dependent relationship between SHM use and mental health outcomes in various settings, including hospitalized patients. For example, benzodiazepines, commonly prescribed for sleep or anxiety, have been associated with depressive mood, highlighting the complex interplay between SHM use and mental health [ 48 ]. Hospitalization can alter drug metabolism, especially in older adults with fluctuating renal and hepatic function, amplifying both cognitive and emotional outcomes. Prior studies show that SHM burden changes- including increases, decreases, or medication switches- affect cognitive function [ 29 ]. Our findings suggest that despite reductions in SHM burden, stressors associated with hospitalization and discharge likely outweighed the benefits, contributing to persistent emotional distress at discharge. Covariates associated with mental outcomes Gender, age, cognitive function, and physical performance were identified as key covariates. Women exhibited higher anxiety levels than men, consistent with research showing gender differences in health concerns and coping mechanisms [ 6 , 49 ]. Older age was linked to lower anxiety but higher depressive mood, possibly reflecting accumulated life experience mitigating stress while ongoing psychological and medical challenges exacerbate depression [ 1 , 11 , 50 , 51 ]. Cognitive and functional status were protective; higher cognitive function was associated with lower depressive mood, and better physical performance was associated with lower anxiety and depressive mood, potentially due to increased self-efficacy and social engagement [ 3 , 4 , 6 ]. Baseline anxiety was controlled in depressive mood analyses and vice versa, revealing a potential bidirectional relationship and emphasizing the need for integrated assessment and early intervention [ 1 , 52 ]. Interestingly, higher comorbidity burden was associated with lower in-hospital anxiety, possibly reflecting familiarity with medical environments. Though anxiety rose post-discharge, such patients may benefit from stabilizing social support. This unexpected association warrants further investigation. Associations observed for covariates such as age should be interpreted cautiously, as effect sizes were modest and may partly reflect differential attrition and sample selection rather than substantive mechanisms. Strengths and limitations The study’s strength lies in its innovative longitudinal design, utilizing repeated-measures mixed models to characterize mental health trajectories across the peri-hospital period, offering a deeper understanding of mental health changes over time. However, there are limitations. First, post-discharge mortality could not be assessed due to ethical restrictions limiting data collection outside the hospital system. Second, although several associations were statistically significant, the magnitude in continuous HADS scores were often small and below the minimal-clinically important difference reported in the literature. Nonetheless, the longitudinal mixed-models approach is well-suited for detecting subtle time-dependent changes and identifying potential intervention points. Third, PSB was not collected post-discharge, necessitating the two-stage analytic approach that separates in-hospital and post-discharge periods. Additionally, reliance on self-reported measures may introduce bias, but these measures provide valuable insight into patients’ subjective experiences as previously demonstrated [ 21 , 29 , 53–55 ]. Excluding patients with severe cognitive impairments limited the generalizability of our findings, although studying cognitively intact patients remains important and may enhance the findings. Participation bias also influenced generalizability, as 181 participants did not complete post-discharge interviews. This attrition of older and more medically complex participants warrants cautious interpretation of covariates with modest effect sizes; as some associations may reflect this bias rather than substantive mechanisms. Lastly, while multiple timepoints were used, more frequent assessments could have provided further insights. Implications and future research This study emphasizes the importance of addressing potentially modifiable factors associated with anxiety and depressive mood during the peri-hospital period, such as SQ and PSB. Future research should incorporate longer follow-up periods, more frequent assessments, and objective sleep measures. Intervention studies targeting sleep, symptom burden, and SHM use could establish causal relationships and inform clinical guidelines. Additionally, exploring qualitative aspects of patients lived experiences with sleep disturbances and mental health during and after hospitalization could reveal unmet needs and barriers to effective care. Conclusions Our findings underline the need to address anxiety and depressive mood in older adults during and after acute hospitalization, as these conditions may worsen post-discharge. SQ and symptom burden significantly associated with mental health, and changes in SHM use were notably associated with depressive mood at discharge. A comprehensive approach, integrating sleep, physical symptoms, and medication management, is crucial to improving mental health outcomes in older adults during hospitalization and recovery. Supplementary Material afag076_Supplemental_File afag076_supplemental_file.pdf (394KB, pdf) Acknowledgements The authors would like to express their gratitude to Dr. Nitza Barkan from the University of Haifa and Mr. Alexander Bogachenko BSc for their valuable support in data analysis. We sincerely appreciate the Cheryl Spencer Institute of Nursing Research and the Graduate Studies Authority—Bloom Graduate School—for their continued support and assistance during the Ph.D. study period. Additionally, we extend our thanks to the entire HoPE-MOR research team and the medical units of Haemek, Bnei-Zion, Carmel, and Rambam hospitals for their contributions. Contributor Information Juliana Smichenko, University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel; Clalit Research Institute, Innovation Division, Clalit Health Services, Tel-Aviv, Tel-Aviv District, 6209804, Israel; University of Haifa - The Center of Research and Study of Aging, Haifa, Haifa District, 3498838, Israel. Anna Zisberg, University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel; University of Haifa - The Center of Research and Study of Aging, Haifa, Haifa District, 3498838, Israel; University of Haifa - ADVANCE Azrieli Advanced Nursing Center, Haifa, Haifa District, 3498838, Israel. Tamar Shochat, University of Haifa - Faculty of Social Welfare and Health Sciences, The Cheryl Spencer Department of Nursing, Haifa, Haifa District, 3498838, Israel. Declaration of Conflicts of Interest None. Declaration of Sources of Funding This work was supported by the Israel Science Foundation ‘(grant number 1216/17)’. The funders played no role in in the design, execution, analysis and interpretation of data, or writing of the study. 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