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Objectives Bipolar disorder (BD) and major depressive disorder (MDD) are characterized by specific alterations of mood. In both disorders, alterations in cognitive domains such as impulsivity, decision‐making, and risk‐taking have been reported. Identification of similarities and differences of these domains in BD and MDD could give further insight into their etiology. The present study assessed impulsivity, decision‐making, and risk‐taking behavior in BD and MDD patients from bipolar multiplex families. Methods Eighty‐two participants (BD type I, n = 25; MDD, n = 26; healthy relatives (HR), n = 17; and healthy controls (HC), n = 14) underwent diagnostic interviews and selected tests of a cognitive battery assessing neurocognitive performance across multiple subdomains including impulsivity (response inhibition and delay aversion), decision‐making, and risk behavior. Generalized estimating equations (GEEs) were used to analyze whether the groups differed in the respective cognitive domains. Results Participants with BD and MDD showed higher impulsivity levels compared to HC; this difference was more pronounced in BD participants. BD participants also showed lower inhibitory control than MDD participants. Overall, suboptimal decision‐making was associated with both mood disorders (BD and MDD). In risk‐taking behavior, no significant impairment was found in any group. Limitations As sample size was limited, it is possible that differences between BD and MDD may have escaped detection due to lack of statistical power. Conclusions Our findings show that alterations of cognitive domains—while present in both disorders—are differently associated with BD and MDD. This underscores the importance of assessing such domains in addition to mere diagnosis of mood disorders.

Depression and cognitive impairment frequently co-occur in older adults, but their temporal relationship remains unclear. While depression is often considered a risk factor for cognitive decline, evidence is mixed, particularly in individuals with mild cognitive impairment or early dementia (MCI/ED). This study analyzed longitudinal data from 1086 participants (M = 74.49, SD = 7.24) in the SMART4MD clinical trial, conducted in Spain and Sweden over 18 months, with assessments every six months. Cognitive impairment was measured using the Mini-Mental State Examination, and depression was assessed with the Geriatric Depression Scale-15. Findings revealed a concurrent association between depressive symptoms and cognitive impairment. In regression mixed analysis, depression levels predicted increased cognitive decline over time, but no evidence was found for cognitive impairment predicting future depression. These associations were confirmed using a bivariate latent growth curve model with cross-lagged paths, which revealed early but attenuating bidirectional effects between depression and cognition. These results highlight depression as a medium-term risk factor for cognitive decline, emphasizing the importance of addressing depressive symptoms to mitigate cognitive deterioration in MCI/ED populations.

Background: Quality of life (QoL) is affected even at early stages in older adults with cognitive impairment. The use of mobile health (mHealth) technology can offer support in daily life and improve the physical and mental health of older adults. However, a clarification of how mHealth technology can be used to support the QoL of older adults with cognitive impairment is needed. Objective: To investigate factors affecting mHealth technology use in relation to self-rated QoL among older adults with cognitive impairment. Methods: A cross-sectional research design was used to analyse mHealth technology use and QoL in 1,082 older participants. Baseline data were used from a multi-centered randomized controlled trial including QoL, measured by the Quality of Life in Alzheimer’s Disease (QoL-AD) Scale, as the outcome variable. Data were analyzed using logistic regression models. Results: Having moderately or high technical skills in using mHealth technology and using the internet via mHealth technology on a daily or weekly basis was associated with good to excellent QoL in older adults with cognitive impairment. Conclusions: The variation in technical skills and internet use among the participants can be interpreted as an obstacle for mHealth technology to support QoL.

Background People with severe mental disorders (SMDs) suffer problems of obesity, a sedentary life, and poor physical condition, mainly due to low levels of physical activity. Self‐efficacy (SE) and social support (SS) are important components that influence participation in physical activity. Methods This study adapted a scale to assess SE and SS in promoting physical activity in Spanish people with SMDs, as well as provide preliminary evidence of its validity. One hundred Spanish patients (23% female) with SMDs, between 26 and 61 years old, completed the SE/SS assessment for SMD (SE/SS‐ASMD). Results The instrument seemed to capture a four‐factor structure in people with SMDs. Due to the lack of a gold standard, the scale was related to other instruments with which it might be expected to show a correlation, such as those for physical activity and its quality; however, the levels of correlation found were low (≈0.3). The Internal consistency (Cronbach's α) for the SE‐ASMD, SS‐ASMD staff, SS‐ASMD peers, and SS‐ASMD family scales were 0.76, 0.76, 0.80, and 0.80, respectively. Conclusions The psychometric analysis of the SE/SS‐ASMD supported its suitability as a new tool for researchers in the area of physical activity among people with SMDs. This study adapted a scale to assess SE and SS in promoting physical activity in Spanish people with SMDs, as well as provide preliminary evidence of its validity.

Impulsivity is a key feature of bipolar disorder (BD) associated with various negative outcomes. Recent use of ecological momentary assessment (EMA) has allowed for nuanced examination of the mechanisms of mood and impulsivity dysregulation. However, few existing studies have used an ecological momentary assessment of impulsivity in multiplex families with BD and examined its associations with mood. Using EMA, this study investigated the concurrent and predictive relationships between impulsivity and mood. Multiplex family members with BD (BDF, n = 8), unaffected family members (FC, n = 6), individuals with BD not from families (BDC, n = 8) and healthy controls (HC, n = 8), completed daily EMA surveys about mood and impulsivity for 6-12 weeks. Mixed-effects regression concurrent and lagged models were employed to analyze the relationship between impulsivity and mood. The BDF (Diff = -31.70, p = 0.001) and BDC (Diff = -25.74, p = 0.007) groups had a significantly lower mean in mood scores compared to the HC group but not compared to the FC group. There were no significant differences in the mean impulsivity scores between the groups. Time-lagged analyses revealed a significant negative association between prior impulsivity and mood at the next assessment independent of diagnosis (OR=0.939, p = 0.002). However, the opposite relationship between prior mood and impulsivity was not significant (OR=0.996, p = 0.135). These results contribute to the understanding of the complex interactions between BD, the genetic load of the disorder, impulsivity and mood. Furthermore, these findings indicate the potential benefits of addressing impulsivity as a means to improve mood outcomes at an early stage.

COVID-19 forced the implementation of restrictive measures in Spain, such as lockdown, home confinement, social distancing, and isolation. It is necessary to study whether limited access to basic services and decreased family and social support could have deleterious effects on cognition, quality of life, and mental health in vulnerable older people. This study aims to explore the impact of the COVID-19 outbreak on cognition in older adults with mild cognitive impairment or dementia as the main outcome and the quality of life, perceived health status, and depression as secondary outcomes and to analyze the association of living alone and a change in living arrangements with those outcomes and other variables related with the use of technology and health services. Likewise, this study aims to analyze the association of high and low technophilia with those variables, to explore the access and use of health care and social support services, and, finally, to explore the informative-, cognitive-, entertainment-, and socialization-related uses of information and communications technologies (ICTs) during the COVID-19 outbreak. This cohort study was conducted in Málaga (Spain). In total, 151 participants with mild cognitive impairment or mild dementia, from the SMART4MD (n=75, 49.7%) and TV-AssistDem (n=76, 50.3%) randomized clinical trials, were interviewed by telephone between May 11 and June 26, 2020. All participants had undergone 1-3 assessments (in 6-month intervals) on cognition, quality of life, and mood prior to the COVID-19 breakout. The outbreak did not significantly impact the cognition, quality of life, and mood of our study population when making comparisons with baseline assessments prior to the outbreak. Perceived stress was reported as moderate during the outbreak. After correction for multiple comparisons, living alone, a change in living arrangements, and technophilia were not associated with negative mental health outcomes. However, being alone was nominally associated with self-perceived fear and depression, and higher technophilia with better quality of life, less boredom, perceived stress and depression, and also less calmness. Overall, health care and social support service access and utilization were high. The most used ICTs during the COVID-19 outbreak were the television for informative, cognitive, and entertainment-related uses and the smartphone for socialization. Our findings show that the first months of the outbreak did not significantly impact the cognition, quality of life, perceived health status, and depression of our study population when making comparisons with baseline assessments prior to the outbreak. Living alone and low technophilia require further research to establish whether they are risk factors of mental health problems during lockdowns in vulnerable populations. Moreover, although ICTs have proven to be useful for informative-, cognitive-, entertainment-, and socialization-related uses during the pandemic, more evidence is needed to support these interventions. ClinicalTrials.gov NCT04385797; https://clinicaltrials.gov/ct2/show/NCT04385797. RR2-10.2196/26431.

There are several mobile health (mHealth) apps in mobile app stores. These apps enter the business-to-customer market with limited controls. Both, apps that users use autonomously and those designed to be recommended by practitioners require an end-user validation to minimize the risk of using apps that are ineffective or harmful. Prior studies have reviewed the most relevant aspects in a tool designed for assessing mHealth app quality, and different options have been developed for this purpose. However, the psychometric properties of the mHealth quality measurement tools, that is, the validity and reliability of the tools for their purpose, also need to be studied. The Consensus-based Standards for the Selection of Health Measurement Instruments (COSMIN) initiative has developed tools for selecting the most suitable measurement instrument for health outcomes, and one of the main fields of study was their psychometric properties. This study aims to address and psychometrically analyze, following the COSMIN guideline, the quality of the tools that are used to measure the quality of mHealth apps. From February 1, 2019, to December 31, 2019, 2 reviewers searched PubMed and Embase databases, identifying mHealth app quality measurement tools and all the validation studies associated with each of them. For inclusion, the studies had to be meant to validate a tool designed to assess mHealth apps. Studies that used these tools for the assessment of mHealth apps but did not include any psychometric validation were excluded. The measurement tools were analyzed according to the 10 psychometric properties described in the COSMIN guideline. The dimensions and items analyzed in each tool were also analyzed. The initial search showed 3372 articles. Only 10 finally met the inclusion criteria and were chosen for analysis in this review, analyzing 8 measurement tools. Of these tools, 4 validated ≥5 psychometric properties defined in the COSMIN guideline. Although some of the tools only measure the usability dimension, other tools provide information such as engagement, esthetics, or functionality. Furthermore, 2 measurement tools, Mobile App Rating Scale and mHealth Apps Usability Questionnaire, have a user version, as well as a professional version. The Health Information Technology Usability Evaluation Scale and the Measurement Scales for Perceived Usefulness and Perceived Ease of Use were the most validated tools, but they were very focused on usability. The Mobile App Rating Scale showed a moderate number of validated psychometric properties, measures a significant number of quality dimensions, and has been validated in a large number of mHealth apps, and its use is widespread. It is suggested that the continuation of the validation of this tool in other psychometric properties could provide an appropriate option for evaluating the quality of mHealth apps.

Non-adherence is common and contributes to adverse health outcomes, reduced quality of life, and increased healthcare expenditure. The objective of this study was to assess the diagnostic validity to estimate the prevalence of non-adherence in patients with mild cognitive impairment (MCI) and dementia using two self-reported methods (SRMs) that are useful and easy in clinical practice, considering the pill count as a reference method (RM). The cohort study was nested in a multicenter randomized controlled trial NCT03325699. A total of 387 patients from 8 health centers were selected using a non-probabilistic consecutive sampling method. Inclusion criteria were as follows: a score of 20-28 points on the Mini-Mental State Examination (MMSE); older than 55 years; taking prescribed medication; and are in charge of their own medication use. Participants were followed up for 18 months after the baseline visit, i.e., 6, 12, and 18 months. Variables related with treatment adherences were measured in all visits. The variables included age, sex, treatment, comorbidities, and the MMSE test. Adherences included pill counts and Morisky-Green test (MGT) and Batalla test (BT) as SRMs. Statistical analysis included descriptive analysis and 95% confidence intervals (CIs). The diagnostic validity included the following: 1) open comparison statistical association between SRMs and RMs and 2) hierarchy comparison: the RM as the best method to assess non-adherence, kappa value (k), sensitivity (S), specificity (Sp), and likelihood ratio (PPV/PPN). A total of 387 patients were recruited with an average age of 73.29 years (95% CI, 72.54-74.04), of which 59.5% were female. Comorbidities were 54.4% HTA, 35.9% osteoarticular pathology, and 24.5% DM. The MMSE mean score was 25.57 (95% CI, 25.34-25.8). The treatment adherence for the RM oscillates between 22.5% in the baseline and 26.3%, 14.8%, and 17.9% in the follow-up visits. For SRMs, the treatment adherence oscillates between 43.5% in the baseline and 32.4%, 21.9%, and 20.3% in the follow-up visits. The kappa value was statistically significant in all the comparison in all visits with a score between 0.16 and 035. Regarding the diagnostic validity, for the MGT, the sensibility oscillated between 0.4 and 0.58, and the specificity oscillated between 0.68 and 0.87; for the BT, the sensibility oscillated between 0.4 and 0.7, and the specificity oscillated between 0.66 and 0.9; and when both tests were used together, the sensibility oscillated between 0.22 and 0.4, and the specificity oscillated between 0.85 and 0.96. SRMs classify non-adherent subjects correctly. They are very easy to use and yield quick results in clinical practice, so SRMs would be used for the non-adherence diagnosis in patients with MCI and mild dementia.

Digital health interventions may improve different behaviours. However, the rapid proliferation of technological solutions often does not allow for a correct assessment of the quality of the tools. This study aims to review and assess the quality of the available mobile applications (apps) related to interventions for low back pain. Two reviewers search the official stores of Android (Play Store) and iOS (App Store) for localisation in Spain and the United Kingdom, in September 2019, searching for apps related to interventions for low back pain. Seventeen apps finally are included. The quality of the apps is measured using the Mobile App Rating Scale (MARS). The scores of each section and the final score of the apps are retrieved and the mean and standard deviation obtained. The average quality ranges between 2.83 and 4.57 (mean 3.82) on a scale from 1 (inadequate) to 5 (excellent). The best scores are found in functionality (4.7), followed by aesthetic content (mean 4.1). Information (2.93) and engagement (3.58) are the worst rated items. Apps generally have good overall quality, especially in terms of functionality and aesthetics. Engagement and information should be improved in most of the apps. Moreover, scientific evidence is necessary to support the use of applied health tools.

Depressive disorders represent the largest proportion of mental illnesses, and by 2030, they are expected to be the first cause of disability-adjusted life years [1]. The COVID-19 pandemic exacerbated prevalence and burden of depression and increased the occurrence of depressive symptoms in general population [2]. The urgency of implementing mental health services to address new barriers to care persuaded clinicians to use telemedicine to follow patients and stay in touch with them, and to explore digital therapeutics (DTx) as potential tools for clinical intervention [2]. The combination of antidepressants and psychotherapy is widely recommended for depression by international guidelines [3] but is less frequently applied in real-world practice. Commonly used treatments are pharmacological, but while being effective, some aspects such as adherence to the drug regimen, residual symptoms, resistance, lack of information, and stigma may hinder successful treatment. In case of less severe depression, standalone psychological therapies should be the first-line treatment option [3], but access to trained psychotherapists remains inequitable. DTx are evidence-based therapies driven by software programs to treat or complement treatment of a specific disease. DTx are classified as Medical Devices, and given their therapeutic purpose, they need to be validated through randomized controlled clinical trials, as for drug-based therapies. In the last 10 years, studies of digital interventions have proliferated; these studies demonstrate that digital interventions increase remission rates and lower the severity of depressive symptoms compared with waitlist, treatment as usual, and attention control conditions [4]. Despite the efficacy demonstrated in clinical trials, many of these tools never reach real-life patients; thus, it might be necessary to implement DTx in the public health system to expand access to valid treatment options. In this framework, DTx represent a good opportunity to help people with depression receive optimal psychotherapeutic care [5].