The relationship between loneliness and depression among college students: Mining data derived from passive sensing.

Background: While there is recognition of the relationship between loneliness and depression, specific behavioural patterns distinguishing both are still not well understood. Objectives: Our objective is to identify distinct behavioural patterns of loneliness and depression from a passively collected dataset of college students, understand their similarities and interrelationships and assess their effectiveness in identifying loneliness and depression. Methods: Utilizing the StudentLife dataset, we applied regression analysis to determine associations with self-reported loneliness and depression. Mediation analysis interprets the relationship between the two conditions, and machine learning models predict loneliness and depression based on behavioural indicators. Results: Distinct behavioural patterns emerged: high evening screen time (OR = 1.45, p = 0.002) and high overall phone usage (OR = 1.50, p = 0.003) were associated with more loneliness, whereas depression was significantly associated with fewer screen unlocks (OR = 0.75, p = 0.044) and visits to fewer unique places (OR = 0.85, p = 0.023). Longer durations of physical activity (OR = 0.72, p = 0.014) and sleep (OR = 0.46, p = 0.002) are linked to a lower risk of both loneliness and depression. Mediation analysis revealed that loneliness significantly increases the likelihood of depression by 48%. The prediction accuracy of our XGBoost-based machine learning approach was 82.43% for loneliness and 79.43% for depression. Conclusion: Our findings show that high evening screen time and overall phone usage are significantly associated with increased loneliness, while fewer screen unlocks and visits to fewer unique places are significantly related to depression. The findings can help in developing targeted interventions to promote well-being and mental health in students.

Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review.

BACKGROUND: Loneliness and social isolation are associated with multiple health problems, including depression, functional impairment, and death. Mobile sensing using smartphones and wearable devices, such as fitness trackers or smartwatches, as well as ambient sensors, can be used to acquire data remotely on individuals and their daily routines and behaviors in real time. This has opened new possibilities for the early detection of health and social problems, including loneliness and social isolation. OBJECTIVE: This scoping review aimed to identify and synthesize recent scientific studies that used passive sensing techniques, such as the use of in-home ambient sensors, smartphones, and wearable device sensors, to collect data on device users' daily routines and behaviors to detect loneliness or social isolation. This review also aimed to examine various aspects of these studies, especially target populations, privacy, and validation issues. METHODS: A scoping review was undertaken, following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Studies on the topic under investigation were identified through 6 databases (IEEE Xplore, Scopus, ACM, PubMed, Web of Science, and Embase). The identified studies were screened for the type of passive sensing detection methods for loneliness and social isolation, targeted population, reliability of the detection systems, challenges, and limitations of these detection systems. RESULTS: After conducting the initial search, a total of 40,071 papers were identified. After screening for inclusion and exclusion criteria, 29 (0.07%) studies were included in this scoping review. Most studies (20/29, 69%) used smartphone and wearable technology to detect loneliness or social isolation, and 72% (21/29) of the studies used a validated reference standard to assess the accuracy of passively collected data for detecting loneliness or social isolation. CONCLUSIONS: Despite the growing use of passive sensing technologies for detecting loneliness and social isolation, some substantial gaps still remain in this domain. A population heterogeneity issue exists among several studies, indicating that different demographic characteristics, such as age and differences in participants' behaviors, can affect loneliness and social isolation. In addition, despite extensive personal data collection, relatively few studies have addressed privacy and ethical issues. This review provides uncertain evidence regarding the use of passive sensing to detect loneliness and social isolation. Future research is needed using robust study designs, measures, and examinations of privacy and ethical concerns.

Factors influencing transition to care homes for people with dementia in Northern Ireland.

INTRODUCTION: The increasing number of people with dementia (PwD) is a significant health and financial challenge for countries. PwD often transition to a care home. This study explored factors predicting transition to care homes for PwD and the place and causes of death. METHODS: Data about dementia medication, care home transitions, demographic characteristics, deaths, and hospital admissions were extracted from national databases from 2010 to 2016. RESULTS: PwD (n = 25,418) were identified through prescriptions of dementia medication, from which 11,930 transitioned to care homes. A logistic regression showed that increased age, female sex, living in less deprived and urban areas, and hospital admissions predicted this transition. PwD who transition to care homes are more likely to die there. The most common cause of death was dementia. DISCUSSION: Certain demographic characteristics are significant predictors for care home transitions and they should be considered in the development of early community-based care services to delay transitions. In the last decades, dementia has been reported more frequently in death certificates.

Living alone for people on dementia medication: related use of drugs.

Approximately one-third of people with dementia in the United Kingdom live alone. People living alone with dementia may receive different treatment for dementia and may have different comorbidities compared to people who live with a caregiver. This study explored differences in medication and demographic characteristics between people living alone with dementia and those living with a caregiver in Northern Ireland. People with dementia were identified through the first date that a dementia management medication was prescribed between 2010 and 2016. In total, 25,418 people were prescribed a dementia management medication. Data for whether people with dementia lived alone was extracted through the National Health Application and Infrastructure Services and from national datasets through the Honest Broker Service. Approximately 35% (n= 8,828) of people with dementia in Northern Ireland lived alone. People with dementia who lived alone were younger (mean= 75 years, SD= 8.50) compared to people who lived with a caregiver (mean= 77 years, SD= 7.82). Binary logistic regression highlighted that people who lived alone were more likely to be treated with donepezil medication for dementia and less likely to receive antidepressants. These findings indicate that living alone did not affect treatment for dementia and comorbidity medication in people on dementia medication.

Factors Associated with Mortality Including Nursing Home Transitions: A Retrospective Analysis of 25,418 People Prescribed Anti-Dementia Drugs in Northern Ireland.

BACKGROUND: Understanding factors associated with mortality after a dementia diagnosis can provide essential information to the person with dementia, their family, and caregivers. To date very little is known about the factors associated with mortality after a dementia diagnosis in Northern Ireland. OBJECTIVE: To determine how demographic and other factors such as deprivation and comorbidity medications influence mortality rates after a dementia diagnosis in Northern Ireland and whether these factors are influenced through nursing home transitions. METHODS: 25,418 people prescribed anti-dementia medication were identified through the enhanced prescribing database between 2010 and 2016. The impact of covariates including age, gender, marital status, deprivation measure, urban/rural classification, and comorbidity medications were examined using cox proportional hazard models with hazard ratios (HR) and 95% confidence intervals. RESULTS: Between 2010 and 2016, 12,129 deaths occurred, with 114 deaths/1,000 person years. Males had significantly higher mortality rates in comparison to females (HR = 1.28; 95% CI = 1.23-1.33); this was true regardless of whether the person with dementia transitioned to a nursing home. People prescribed anti-dementia drugs living with lower levels of deprivation had significantly lower mortality rates in comparison to people living with the highest levels of deprivation (HR = 0.93; 95% CI = 0.89-0.97). Diabetic (HR = 1.18; 95% CI = 1.07-1.29) and anti-arrhythmic (HR = 2.44; 95% CI = 1.01-5.91) medication in particular significantly influenced mortality. CONCLUSION: Male gender, higher comorbidity medications, and living in areas of higher deprivation significantly increased mortality rates for people prescribed anti-dementia drugs in our study population. When comorbidity medications were classified, only anti-arrhythmia and diabetic medications significantly increased mortality. Future research should continue to investigate factors which influence mortality after a dementia diagnosis.