The impact and measurement of social dysfunction in late-life depression: an evaluation of current methods with a focus on wearable technology

Daily vitality fluctuations in older adults with depressive symptoms: A multilevel location-scale model

BACKGROUND

Examining the daily experiences of older adults with depression facilitates the development and application of personalized effective treatments for them. In previous clinical research on depression, traditional mean-based approaches have mainly been employed. However, the within-person residual variance as a random effect provides greater insight into the heterogeneity of daily experiences among geriatric samples.

OBJECTIVE

This study aimed to examine the relationship between depression and daily vitality in older adults. Specifically, it focused on the mean and residual variance of daily vitality measured by the Ecological Momentary Assessment (EMA).

METHODS

Data from 64 older adults aged 65 years or more, who participated in community welfare centers or retirees' associations, were used. Daily vitality was examined using EMA surveys for seven consecutive days (four random surveys per day). The data were analyzed using a location-scale model.

RESULTS

The intraclass correlation computed from the empty model for the EMA data was 0.488, indicating significant variances in daily vitality across time between individuals. Older adults with higher levels of depressive symptoms showed low mean levels of daily vitality and a large log-residual variance of daily vitality.

CONCLUSIONS

The findings from the current study suggest that individuals experiencing depression not only exhibit low vitality in their daily lives but also struggle to maintain stable levels of vitality in their lives. These insights could contribute to the facilitation and advancement of personalized interventions tailored for older adults.

A predictive model for depression in Chinese middle-aged and elderly people with physical disabilities

BACKGROUND

Middle-aged and older adults with physical disabilities exhibit more common and severe depressive symptoms than those without physical disabilities. Such symptoms can greatly affect the physical and mental health and life expectancy of middle-aged and older persons with disabilities.

METHOD

This study selected 2015 and 2018 data from the China Longitudinal Study of Health and Retirement. After analyzing the effect of age on depression, we used whether middle-aged and older adults with physical disabilities were depressed as the dependent variable and included a total of 24 predictor variables, including demographic factors, health behaviors, physical functioning and socialization, as independent variables. The data were randomly divided into training and validation sets on a 7:3 basis. LASSO regression analysis combined with binary logistic regression analysis was performed in the training set to screen the predictor variables of the model. Construct models in the training set and perform model evaluation, model visualization and internal validation. Perform external validation of the model in the validation set.

RESULT

A total of 1052 middle-aged and elderly persons with physical disabilities were included in this study, and the prevalence of depression in the elderly group > middle-aged group. Restricted triple spline indicated that age had different effects on depression in the middle-aged and elderly groups. LASSO regression analysis combined with binary logistic regression screened out Gender, Location of Residential Address, Shortsightedness, Hearing, Any possible helper in the future, Alcoholic in the Past Year, Difficulty with Using the Toilet, Difficulty with Preparing Hot Meals, and Unable to work due to disability constructed the Chinese Depression Prediction Model for Middle-aged and Older People with Physical Disabilities. The nomogram shows that living in a rural area, lack of assistance, difficulties with activities of daily living, alcohol abuse, visual and hearing impairments, unemployment and being female are risk factors for depression in middle-aged and older persons with physical disabilities. The area under the ROC curve for the model, internal validation and external validation were all greater than 0.70, the mean absolute error was less than 0.02, and the recall and precision were both greater than 0.65, indicating that the model performs well in terms of discriminability, accuracy and generalisation. The DCA curve and net gain curve of the model indicate that the model has high gain in predicting depression.

CONCLUSION

In this study, we showed that being female, living in rural areas, having poor vision and/or hearing, lack of assistance from others, drinking alcohol, having difficulty using the restroom and preparing food, and being unable to work due to a disability were risk factors for depression among middle-aged and older adults with physical disabilities. We developed a depression prediction model to assess the likelihood of depression in Chinese middle-aged and older adults with physical disabilities based on the above risk factors, so that early identification, intervention, and treatment can be provided to middle-aged and older adults with physical disabilities who are at high risk of developing depression.

Perspectives of Japanese elders and their healthcare providers on use of wearable technology to monitor their health at home: A qualitative exploration

BACKGROUND

With 24 million Japanese elderly aging at home, the challenges of managing chronic conditions are significant. As many Japanese elders manage multiple chronic conditions, investigating the usefulness of wearable health devices for this population is warranted.

AIM

The purpose of this qualitative study, using grounded theory, was to explore the perspectives of Japanese elders, their caretakers, and their healthcare providers on the use of technology and wearable devices to monitor health conditions and keep Japanese elders safe at home.

METHODS

In conducting this study, a community advisory board was first established to guide the research design; six focus groups and two one-on-one interviews were conducted, with a total of 21 participants.

RESULTS

Four major themes emerged from the analysis: 1) Current Status of Health Issues Experienced by Japanese Elders and Ways of Being Monitored; 2) Current Use of Monitoring Technology and Curiosity about Use of the Latest Digital Technology to Keep Elderly Healthy at Home; 3) Perceived Advantages of Wearing Sensor Technology; and 4) Perceived Disadvantages of Wearing Technology. Many of the elderly participants were interested in using monitoring devices at home, particularly if not complicated. Healthcare workers found monitoring technologies particularly useful during the isolation of the COVID-19 pandemic. Elderly participants felt cost and technical issues could be barriers to using monitoring devices.

CONCLUSION

While there are challenges to utilizing monitoring devices, the potential to aid the aging population of Japan justifies further investigation into the effectiveness of these devices. This study was not registered with a research trial registry.

A nurse-led mHealth intervention to alleviate depressive symptoms in older adults living alone in the community: A quasi-experimental study

BACKGROUND

The prevalence of geriatric depression has increased worldwide, becoming a major contributor to the burden of health care costs. Geriatric depression is difficult to detect in daily life because of its atypical presentation for each person. Therefore, there is an emerging need to develop personalised mHealth interventions for older adults with depression based on data from an ecological momentary assessment.

OBJECTIVE

To develop and evaluate the effect of a nurse-led mHealth intervention of geriatric depression in older adults living alone.

DESIGN

A quasi-experimental research design was used, and the study followed the transparent reporting of evaluations with a nonrandomised design statement.

SETTING

The nurse-led mHealth intervention was developed and evaluated in a community senior centre in Seoul, Korea.

PARTICIPANTS

Sixty-four older adults living alone with depressive symptoms were recruited between 1 October 2018 and 1 October 2019.

METHODS

Study participants were randomly assigned to the intervention or control groups by drawing lots. In the intervention group, nurses repeatedly assessed older adults' depressive symptoms using an ecological momentary assessment via a mobile tablet. The intervention consisted of weekly sessions, which included (1) standardised mHealth device training, (2) a nurse-led mHealth programme, and (3) art activities. The control group received care as usual. Intra- and inter-group differences were evaluated using paired t-tests and analysis of covariance was used to assess subjective depression symptoms. A linear mixed-model was used to analyse the relationship between groups and momentary scores over time.

RESULTS

The average age of the final sample was 76.2 years (SD = 6.06), 63.6 % (28/44) of whom were female. Compared with the control group (n = 23), the intervention group (n = 21) showed a decreased depression score (t = 4.041, p = .027). There was no statistical difference between the intervention and control groups based on traditional scales and the ecological momentary assessment. However, our data from the ecological momentary assessment captures clear fluctuating patterns across the days during the study, which traditional scales could not measure.

CONCLUSIONS

Most of the older adults successfully participated in a nurse-led mHealth intervention that included multiple components of a non-pharmacological approach to address depression. Mental health nurses should perform critical roles to personalise mHealth activities considering the older adult's autonomy and supportive decision-making, specifically when using high-technological intervention. Future research should maximise the methodological and clinical advantage of an ecological momentary assessment of geriatric depression.

REGISTRATION

Clinical Research Information Service number KCT0005073.

Changes in behavioral activities and transition of depressive symptoms among younger-old community-dwelling adults during 6 years: An age-specific prospective cohort study

OBJECTIVE

Regular engagement in behavioral activities plays a crucial role against depressive symptoms in older adults. This study aims to explore the relationship between behavioral activities and the temporal evolution of depressive symptoms.

METHODS

We included community-dwelling Japanese adults aged 64 or 65 years with and without depressive symptoms enrolled in the New Integrated Suburban Seniority Investigation (NISSIN) project. Depressive symptoms at baseline and follow-up were assessed using the 15-item Geriatric Depression Scale. Behavioral activities were measured by self-reported questions. Risk ratios and 95% confidence intervals were calculated using modified Poisson regression, adjusting for relevant sociodemographic variables and health-related confounders.

RESULTS

During the 6 year follow-up period, 139 (10.1%) without depressive symptoms at baseline developed such symptoms over time, while 174 (51.6%) with depressive symptoms improved to the point of these symptoms being absent. The participants without depressive symptoms at baseline and those who engaged in social activity or daily walking at a continued regular frequency (CRF) or an increased frequency (IF) and exercise habits at CRF were the least likely to have depressive symptoms onset at follow-up. There was no significant difference between the changes in behavioral activities and the improvement of depressive symptoms after controlling for confounders. Participants engaging in a greater variety of behavioral activities at CRF were less likely to experience a new onset of depressive symptoms.

CONCLUSIONS

Consistent and regular participation in one or more behavioral activities was significantly associated with the onset of depressive symptoms in Japanese community-dwelling older adults.

Improving mental health and social participation outcomes in older adults with depression and anxiety: Study protocol for a randomised controlled trial

Background

Increasing both the frequency and quality of social interactions within treatments for anxiety and depressive disorders in older adults may improve their mental health outcomes and quality of life. This study aims to evaluate the clinical efficacy and cost utility of an enhanced cognitive behavioural therapy (CBT) plus social participation program in a sample of older adults with depression and/or anxiety.

Methods

A total of 172 community-dwelling adults aged 65 years or older with an anxiety and/or depressive disorder will be randomly allocated to either an enhanced CBT plus social participation program (n = 86) or standard CBT (n = 86). Both treatments will be delivered during 12 weekly individual sessions utilising structured manuals and workbooks. Participants will be assessed at pre-treatment, post-treatment, and 12-month follow-up. The primary outcome evaluates mean change in clinician-rated diagnostic severity of anxiety and depressive disorders from baseline to post-treatment (primary endpoint) based on a semi-structured diagnostic interview. Secondary outcomes evaluate changes in symptomatology on self-report anxiety and depression measures, as well as changes in social/community participation, social network, and perceived social support, loneliness, quality of life, and use of health services. Economic benefits will be evaluated using a cost-utility analysis to derive the incremental cost utility ratios for the enhanced CBT program.

Discussion

Outcomes from this study will provide support for the establishment of improved psychosocial treatment for older adults with anxiety and/or depression. Study outcomes will also provide health systems with a clear means to reduce the impact of poor emotional health in older age and its associated economic burden. In addition to the empirical validation of a novel treatment, the current study will contribute to the current understanding of the role of social participation in older adult wellbeing.

Trial registration

Prospectively registered on the Australian New Zealand Clinical Trials Registry (ID: ACTRN12619000242123; registered 19^th February 2019) and the ISRCTN registry (ID: ISRCTN78951376; registered 10^th July 2019).

Abnormal Insular Dynamic Functional Connectivity and Its Relation to Social Dysfunctioning in Children With Attention Deficit/Hyperactivity Disorder

Anomalies in large-scale cognitive control networks impacting social attention abilities are hypothesized to be the cause of attention deficit hyperactivity disorder (ADHD). The precise nature of abnormal brain functional connectivity (FC) dynamics including other regions, on the other hand, is unknown. The concept that insular dynamic FC (dFC) among distinct brain regions is dysregulated in children with ADHD was evaluated using Insular subregions, and we studied how these dysregulations lead to social dysfunctioning. Data from 30 children with ADHD and 28 healthy controls (HCs) were evaluated using dynamic resting state functional magnetic resonance imaging (rs-fMRI). We evaluated the dFC within six subdivisions, namely both left and right dorsal anterior insula (dAI), ventral anterior insula (vAI), and posterior insula (PI). Using the insular sub-regions as seeds, we performed group comparison between the two groups. To do so, two sample t-tests were used, followed by post-hoc t-tests. Compared to the HCs, patients with ADHD exhibited decreased dFC values between right dAI and the left middle frontal gyrus, left postcentral gyrus and right of cerebellum crus, respectively. Results also showed a decreased dFC between left dAI and thalamus, left vAI and left precuneus and left PI with temporal pole. From the standpoint of the dynamic functional connectivity of insular subregions, our findings add to the growing body of evidence on brain dysfunction in ADHD. This research adds to our understanding of the neurocognitive mechanisms behind social functioning deficits in ADHD. Future ADHD research could benefit from merging the dFC approach with task-related fMRI and non-invasive brain stimulation, which could aid in the diagnosis and treatment of the disorder.

Stress and Coping in Older Australians During COVID-19: Health, Service Utilization, Grandparenting, and Technology Use

OBJECTIVES

This study examined the impacts of COVID-19 lockdown on health and lifestyle factors for older adults in Sydney, Australia. The study examined demographic differences, social engagement, loneliness, physical activity, emotion regulation, technology use, and grandparenting experiences and their contribution to emotional health and quality of life during lockdown.

METHODS

Participants were 201 community-dwelling older adults (60-87 years, M = 70.55, SD = 6.50; 67.8% female) who completed self-report scales measuring physical and emotional health outcomes, quality of life, health service utilization, changes in diet and physical activity, impacts on grandparenting roles, and uptake of new technology.

RESULTS

One-third of older adults experienced depression, and 1 in 5 experienced elevated anxiety and/or psychological distress during lockdown. Specific emotion regulation strategies, better social and family engagement, and new technology use were associated with better emotional health and quality of life; 63% of older adults used new technologies to connect with others.

CONCLUSIONS

Older adults were adaptable and resilient during lockdown, demonstrating high uptake of new technologies to remain connected to others, while negative emotional health outcomes were linked to loneliness and unhelpful emotion regulation.

CLINICAL IMPLICATIONS

Further diversifying use of video technologies may facilitate improved physical and emotional health outcomes.

The major risk factor for depression in the Chinese middle-aged and elderly population: A cross-sectional study

BACKGROUND

The number of patients suffering from depression is continuously increasing in China. Demographic characteristics, physical health levels, and individual lifestyles/healthy behaviors are associated with the severity of depression. However, the major risk factor for depression remains unclear.

MATERIALS AND METHODS

In this investigation, 16,512 patients were screened using the CHARLS (China Health and Retirement Longitudinal Study) database after being determined to be eligible based on the inclusion criteria. Depressive symptoms were evaluated through the CESD-10 (10-item Center for Epidemiological Studies Depression Scale). Consequently, various models were developed based on potential predictive factors, employing stepwise LR (Logistic Regression)/RF (Random Forests) models to examine the influence and weighting of candidate factors that affect depression.

RESULTS

Gender, residential address location, changes in health status following last interview, physical disabilities, chronic pain, childhood health status, ADL (activity of daily living), and social activity were all revealed to be independent risk factors for depression (p < 0.05) in this study. Depression has a synergic effect (across chronic pain and age groups). In comparison to other factors, RF results showed that chronic pain had a stronger impact on depression.

CONCLUSION

This preliminary study reveals that chronic pain is a major risk factor for depression.

Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.

Impact of Fluoxetine on Behavioral Invigoration of Appetitive and Aversively Motivated Responses: Interaction With Dopamine Depletion

Impaired behavioral activation and effort-related motivational dysfunctions like fatigue and anergia are debilitating treatment-resistant symptoms of depression. Depressed people show a bias towards the selection of low effort activities. To determine if the broadly used antidepressant fluoxetine can improve behavioral activation and reverse dopamine (DA) depletion-induced anergia, male CD1 mice were evaluated for vigorous escape behaviors in an aversive context (forced swim test, FST), and also with an exercise preference choice task [running wheel (RW)-T-maze choice task]. In the FST, fluoxetine increased active behaviors (swimming, climbing) while reducing passive ones (immobility). However, fluoxetine was not effective at reducing anergia induced by the DA-depleting agent tetrabenazine, further decreasing vigorous climbing and increasing immobility. In the T-maze, fluoxetine alone produced the same pattern of effects as tetrabenazine. Moreover, fluoxetine did not reverse tetrabenazine-induced suppression of RW time but it reduced sucrose intake duration. This pattern of effects produced by fluoxetine in DA-depleted mice was dissimilar from devaluing food reinforcement by pre-feeding or making the food bitter since in both cases sucrose intake time was reduced but animals compensated by increasing time in the RW. Thus, fluoxetine improved escape in an aversive context but decreased relative preference for active reinforcement. Moreover, fluoxetine did not reverse the anergic effects of DA depletion. These results have implications for the use of fluoxetine for treating motivational symptoms such as anergia in depressed patients.

Deep learning-based automated speech detection as a marker of social functioning in late-life depression

BACKGROUND

Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device to measure speech that participants encounter as an indicator of social interaction.

METHODS

Twenty nine participants with LLD and 29 age-matched controls wore a wrist-worn device continuously for seven days, which recorded their acoustic environment. Acoustic data were automatically analysed using deep learning models that had been developed and validated on an independent speech dataset. Total speech activity and the proportion of speech produced by the device wearer were both detected whilst maintaining participants' privacy. Participants underwent a neuropsychological test battery and clinical and self-report scales to measure severity of depression, general and social functioning.

RESULTS

Compared to controls, participants with LLD showed poorer self-reported social and general functioning. Total speech activity was much lower for participants with LLD than controls, with no overlap between groups. The proportion of speech produced by the participants was smaller for LLD than controls. In LLD, both speech measures correlated with attention and psychomotor speed performance but not with depression severity or self-reported social functioning.

CONCLUSIONS

Using this device, LLD was associated with lower levels of speech than controls and speech activity was related to psychomotor retardation. We have demonstrated that speech activity measured by wearable technology differentiated LLD from controls with high precision and, in this study, provided an objective measure of an aspect of real-world social functioning in LLD.

Effect of Group Cognitive Behavioural Therapy on Loneliness in a Community Sample of Older Adults: A Secondary Analysis of a Randomized Controlled Trial

OBJECTIVES

Research suggests a link between loneliness, depression, and anxiety. Multiple studies have examined treatment programs for loneliness; however, none have examined the efficacy of Cognitive Behavioral Therapy (CBT) for depression and anxiety in reducing loneliness.

METHODS

Change in loneliness in sixty-two older adults (≥60 yrs; 65% female) who took part in a previously reported randomized controlled trial for the treatment of comorbid depression and anxiety was examined. Older adults were randomized to a 12-week group CBT or waitlist control condition. Participants who took part in CBT were followed-up three months later.

RESULTS

Linear Mixed Model analyses indicated that after controlling for baseline cognition, depression, and anxiety, participants who completed CBT experienced a significant decrease in loneliness while the control group did not. This reduction was maintained at follow-up.

CONCLUSIONS

CBT programs for depression and anxiety are likely to be effective at reducing loneliness. This may be due to shared underlying cognitive and behavioral mechanisms between loneliness, depression, and anxiety such as sensitivity to perceived threat and social withdrawal. Further research is needed to understand if specific loneliness interventions are more effective.

CLINICAL IMPLICATIONS

CBT may be effective at reducing loneliness among older adults with depression and anxiety.

Use of Antidepressants and Risk of Cutaneous Melanoma: A Prospective Registry-Based Case-Control Study

Purpose

Melanoma is the cancer with the most rapidly rising incidence rate in Norway. Although exposure to ultraviolet radiation (UVR) is the major environmental risk factor, other factors may also contribute. Antidepressants have cancer inhibiting and promoting side effects, and their prescription rates have increased in parallel with melanoma incidence. Thus, we aimed to prospectively examine the association between use of antidepressants and melanoma by using nation-wide data from the Cancer Registry of Norway, the National Registry, the Norwegian Prescription Database and the Medical Birth Registry of Norway.

Patient and Methods

All cases aged 18–85 with a primary cutaneous invasive melanoma diagnosed during 2007–2015 (n=12,099) were matched to population controls 1:10 (n=118,467) by sex and year of birth using risk-set sampling. We obtained information on prescribed antidepressants and other potentially confounding drug use (2004–2015). Conditional logistic regression was used to estimate adjusted rate ratios (RRs) and 95% confidence intervals (CIs) for the association between overall and class-specific use of antidepressants and incident melanoma.

Results

Compared with ≤1 prescription, ≥8 prescriptions of antidepressants overall were negatively associated with melanoma (RR 0.81 CI 0.75–0.87). Class-specific analyses showed decreased RRs for selective serotonin reuptake inhibitors (RR 0.82 CI 0.73–0.93) and mixed antidepressants (RR 0.77 CI 0.69–0.86). The negative association was found for both sexes, age ≥50 years, residential regions with medium and highest ambient UVR exposure, all histological subtypes, trunk, upper and lower limb sites and local disease.

Conclusion

Use of antidepressants was associated with decreased risk of melanoma. There are at least two possible explanations for our results; cancer-inhibiting actions induced by the drug and less UVR exposure among the most frequent users of antidepressants.

The Dietary Inflammatory Index, shift work, and depression: Results from NHANES

OBJECTIVES

Abnormal physiology (e.g., inflammation), brought on by environmental exposures (e.g., diet or shift work [SW]), can affect numerous bodily systems, including the brain, and may be associated with depressive symptomatology. The study examined the associations between SW and depressive symptoms and diet-related inflammation (estimated by the Dietary Inflammatory Index [DII]) and depressive symptoms. Additionally, diet was examined as a mediator between SW and depressive symptoms.

METHOD

Data were obtained from the U.S. National Health and Nutrition Examination Survey (Centers for Disease Control and Prevention, 2013). SW data were based on self-report. Dietary data were collected using 24-hr dietary recalls for DII calculation. Depressive symptoms were defined using a cut-point of 10 (moderate) on the Patient Health Questionnaire-9 (PHQ-9). Logistic regression was used to estimate odds ratios and 95% confidence intervals (95% CI) for depressive symptoms by SW and DII quartiles.

RESULTS

DII scores were associated with depressive symptoms among women. Women in DII quartile 4 were 30% more likely to report depressive symptoms than women in quartile 1 (95% CI [1.00-1.68]). There was no association between symptoms and SW when using a PHQ-9 cut-point of 10. When using a cut-point of 5 (mild depressive symptoms), those working any form of SW were more likely to suffer from mild symptoms than day workers (odds ratio = 1.22; 95% CI [1.04-1.43]). There was some evidence for mediation by the DII between SW and depressive symptoms.

CONCLUSIONS

Future longitudinal studies should examine effects of reductions in inflammation through diet on depressive symptoms, especially among shift workers, to elucidate the role of diet on depression among these groups. (PsycINFO Database Record