We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.
To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure [email protected]
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
Children practice coping every day in response to stressors big and small. Coping develops iteratively with repeated exposure to developmentally normative stressors. The everyday perspective on coping focuses on the immediate functions of coping. Children’s experiences with various coping strategies in daily life shape the development of coping over the long term. The interpersonal perspective on coping focuses on the involvement of close others, including peers and family members, in children’s coping. Interactions with others are intertwined with and shape children’s responses to stressful events. The participation of peers and family members in children’s coping is connected to the adaptiveness of their responses in the short term, and their psychological well-being over the long term. These perspectives inform the conceptualization and measurement of coping. Moreover, they provide suggestions for interventions and the direction of future research on coping development.
Background: Mental resilience refers to the capacity to overcome the negative effects of setbacks and associated stress on performance. In the face of stressors, lack of mental resilience may even cause psychopathology, such as depression. While all combatants are exposed to stressors, female combatants face additional challenges compared with their male counterparts. Resilience is often measured using retrospective self-reports, which do not consider ecological fluctuations across situations and environments. A mobile ecological momentary monitoring allowed us to study gender differences in factors contributing to resilience.
Objectives
Objective: We aimed to characterize gender differences in resilience trajectory in combatants using ecological momentary assessments (EMA).
Methods
Methods: 156 Combatants (98F, 58M) completed mood EMA daily for two weeks using a mobile app. In addition, resilience, QOL and mental health questionnaires were administered three times in four weeks. Stepwise regression models were used to predict resilience after 2-4 weeks.
Results
Results: Female combatants reported higher levels of anxiety and lower resilience, self efficacy and QOL, as well as higher mood variability over time (t(149)=4.9, p<.0001). In addition, while for females, baseline anxiety, self-efficacy and mood EMA all contributed to resilience prediction (37% of variance explained), baseline anxiety was the sole predictor for males (explaining 28% of variance).
Conclusions
Conclusion: Gender differences in resilience were found in combatants who participate in the same occupation. These results emphasize the importance of considering the inclusion smartphone-delivered EMA tools in QOL models.
Ambulatory monitoring is gaining popularity in mental and somatic health care to capture an individual's wellbeing or treatment course in daily-life. Experience sampling method collects subjective time-series data of patients' experiences, behavior, and context. At the same time, digital devices allow for less intrusive collection of more objective time-series data with higher sampling frequencies and for prolonged sampling periods. We refer to these data as parallel data. Combining these two data types holds the promise to revolutionize health care. However, existing ambulatory monitoring guidelines are too specific to each data type, and lack overall directions on how to effectively combine them.
Methods
Literature and expert opinions were integrated to formulate relevant guiding principles.
Results
Experience sampling and parallel data must be approached as one holistic time series right from the start, at the study design stage. The fluctuation pattern and volatility of the different variables of interest must be well understood to ensure that these data are compatible. Data have to be collected and operationalized in a manner that the minimal common denominator is able to answer the research question with regard to temporal and disease severity resolution. Furthermore, recommendations are provided for device selection, data management, and analysis. Open science practices are also highlighted throughout. Finally, we provide a practical checklist with the delineated considerations and an open-source example demonstrating how to apply it.
Conclusions
The provided considerations aim to structure and support researchers as they undertake the new challenges presented by this exciting multidisciplinary research field.
To investigate global and momentary effects of a tablet-based non-pharmacological intervention for nursing home residents living with dementia.
Design:
Cluster-randomized controlled trial.
Setting:
Ten nursing homes in Germany were randomly allocated to the tablet-based intervention (TBI, 5 units) or conventional activity sessions (CAS, 5 units).
Participants:
N = 162 residents with dementia.
Intervention:
Participants received regular TBI (n = 80) with stimulating activities developed to engage people with dementia or CAS (n = 82) for 8 weeks.
Measurements:
Apathy Evaluation Scale (AES-I, primary outcome), Quality of Life in Alzheimer’s Disease scale, QUALIDEM scale, Neuropsychiatric Inventory, Geriatric Depression Scale, and psychotropic medication (secondary outcomes). Momentary quality of life was assessed before and after each activity session. Participants and staff were blinded until the collection of baseline data was completed. Data were analyzed with linear mixed-effects models.
Results:
Levels of apathy decreased slightly in both groups (mean decrease in AES-I of .61 points, 95% CI −3.54, 2.33 for TBI and .36 points, 95% CI −3.27, 2.55 for CAS). Group difference in change of apathy was not statistically significant (β = .25; 95% CI 3.89, 4.38, p = .91). This corresponds to a standardized effect size (Cohen’s d) of .02. A reduction of psychotropic medication was found for TBI compared to CAS. Further analyses revealed a post-intervention improvement in QUALIDEM scores across both groups and short-term improvements of momentary quality of life in the CAS group.
Conclusions:
Our findings suggest that interventions involving tailored activities have a beneficial impact on global and momentary quality of life in nursing home residents with dementia. Although we found no clear advantage of TBI compared to CAS, tablet computers can support delivery of non-pharmacological interventions in nursing homes and facilitate regular assessments of fluctuating momentary states.
To extend evidence on the short-term variability of passive and active suicidal ideation (SI) and the association with suggested proximal risk factors such as interpersonal variables (perceived burdensomeness [PB], thwarted belongingness [TB], hopelessness, and depression) in real-time.
Methods:
This is an observational study using a prospective design applying ecological momentary assessments (EMA). Eligible for study inclusion were inpatients with unipolar depression, current or lifetime suicidal ideation, and fluent German. Over six days, 74 participants rated their momentary level of passive and active SI, PB, TB, depressiveness, and hopelessness up to 10 times per day on smartphones. Data was collected from August 2015 to July 2017. Compliance was excellent (89.7%).
Results:
Mean squared successive differences supported temporal instability for all variables. According intra-class correlations, between 25% and 47% of variance was accounted for by within-person variability. Multilevel analysis demonstrated significant positive associations between hopelessness, depressiveness, PB, and TB with passive SI. Prospectively, hopelessness and PB remained predictors of passive SI. For active SI, hopelessness, depression, PB, and TB were significantly associated cross-sectionally. Prospectively, hopelessness, PB, and the interaction PBxTB predicted active SI. All models were controlled for previous level of SI.
Conclusions:
This study provides further evidence on the short-term variability of SI in very short time frames implying the need of assessing SI repeatedly in clinical and research settings. The associations between interpersonal variables and passive and active SI were only partial in line with assumptions of the Interpersonal Theory of Suicide. Overall, the effects were small warranting further investigation.
Recommend this
Email your librarian or administrator to recommend adding this to your organisation's collection.