Grumpy or depressed? Disentangling typically developing adolescent mood from prodromal depression using experience sampling methods

Within-family associations of parent-adolescent relationship quality and adolescent affective well-being

BACKGROUND

Parent-adolescent relationship quality is theorized to be an important correlate of adolescent affective well-being. Little is known about the within-family processes underlying parent-adolescent relationship quality and affective well-being over a period of months. This three-wave, preregistered study examined within- and between-family associations between parent-adolescent relationship quality (support and conflict) and adolescent well-being (negative and positive affect). In addition, we examined whether the associations differed between mothers and fathers, and for adolescents' affective well-being in different social contexts (at home, at school, with peers).

METHODS

The sample consisted of 244 Dutch adolescents (61.5% girls; age range: 12-17 years; mean age = 13.8 years). Random-intercept cross-lagged panel models were used.

RESULTS

At the between-family level, higher levels of support and lower levels of conflict were associated with higher levels of positive affect and lower levels of negative affect. At the within-family level, increases in support and decreases in conflict were concurrently associated with increases in positive affect and decreases in negative affect. More parent-adolescent conflict than typical also predicted increases in negative affect, 3 months later, and more negative affect and less positive affect than typical predicted increased conflict, 3 months later. These within-family effects were largely similar for fathers and mothers. Associations for conflict occurred through bidirectional processes: Parent-adolescent conflict shaped and was shaped by adolescents' emotions at home, at school, and with peers.

CONCLUSION

Results suggest that parent-adolescent relationship quality (especially conflict) and adolescent affective well-being cofluctuate and predict each other over time within families.

A cognitive-computational account of mood swings in adolescence

Teenagers have a reputation for being fickle, in both their choices and their moods. This variability may help adolescents as they begin to independently navigate novel environments. Recently, however, adolescent moodiness has also been linked to psychopathology. Here, we consider adolescents' mood swings from a novel computational perspective, grounded in reinforcement learning (RL). This model proposes that mood is determined by surprises about outcomes in the environment, and how much we learn from these surprises. It additionally suggests that mood biases learning and choice in a bidirectional manner. Integrating independent lines of research, we sketch a cognitive-computational account of how adolescents' mood, learning, and choice dynamics influence each other, with implications for normative and psychopathological development.

The Mood and Resilience in Offspring (MARIO) project: a longitudinal cohort study among offspring of parents with and without a mood disorder

BACKGROUND

One of the most robust risk factors for developing a mood disorder is having a parent with a mood disorder. Unfortunately, mechanisms explaining the transmission of mood disorders from one generation to the next remain largely elusive. Since timely intervention is associated with a better outcome and prognosis, early detection of intergenerational transmission of mood disorders is of paramount importance. Here, we describe the design of the Mood and Resilience in Offspring (MARIO) cohort study in which we investigate: 1. differences in clinical, biological and environmental (e.g., psychosocial factors, substance use or stressful life events) risk and resilience factors in children of parents with and without mood disorders, and 2. mechanisms of intergenerational transmission of mood disorders via clinical, biological and environmental risk and resilience factors.

METHODS

MARIO is an observational, longitudinal cohort study that aims to include 450 offspring of parents with a mood disorder (uni- or bipolar mood disorders) and 100-150 offspring of parents without a mood disorder aged 10-25 years. Power analyses indicate that this sample size is sufficient to detect small to medium sized effects. Offspring are recruited via existing Dutch studies involving patients with a mood disorder and healthy controls, for which detailed clinical, environmental and biological data of the index-parent (i.e., the initially identified parent with or without a mood disorder) is available. Over a period of three years, four assessments will take place, in which extensive clinical, biological and environmental data and data on risk and resilience are collected through e.g., blood sampling, face-to-face interviews, online questionnaires, actigraphy and Experience Sampling Method assessment. For co-parents, information on demographics, mental disorder status and a DNA-sample are collected.

DISCUSSION

The MARIO cohort study is a large longitudinal cohort study among offspring of parents with and without mood disorders. A unique aspect is the collection of granular data on clinical, biological and environmental risk and resilience factors in offspring, in addition to available parental data on many similar factors. We aim to investigate the mechanisms underlying intergenerational transmission of mood disorders, which will ultimately lead to better outcomes for offspring at high familial risk.

Quaranteens: Prepandemic relationship quality and changes in adolescent internalizing problems during the COVID-19 pandemic

This preregistered longitudinal study examined changes in adolescents' depressive and anxiety symptoms before and during the COVID-19 pandemic using latent additive piece-wise growth models. It also assessed whether support from and conflict with mothers, fathers, siblings, and best friends explained heterogeneity in change patterns. One hundred and ninety-two Dutch adolescents (Mean age: 14.3 years; 68.8% female) completed online biweekly questionnaires for a year (November 2019-October 2020), consisting of a prepandemic, lockdown, and reopening phase. Depressive symptoms increased following the lockdown and decreased upon reopening. Anxiety symptoms showed an immediate decrease followed by a gradual increase in the reopening phase. Prepandemic family and best friend support and conflict did not explain heterogeneity in depressive and anxiety symptoms during the COVID-19 pandemic.

Inequalities in health complaints: 20-year trends among adolescents in Scotland, 1998-2018

This study examined trends in inequalities in health complaints among early adolescents in Scotland from 1998 to 2018. We analysed data from the Health Behaviour in School-aged Children (HBSC) survey conducted in Scotland in 1998, 2002, 2006, 2010, 2014 and 2018. A self-report questionnaire was administered in schools to a nationally representative sample of 11-, 13-, and 15-year-olds (n = 29,250). Health complaints were measured using a scale comprising four psychological symptoms (feeling low, feeling nervous, irritability and sleep difficulties) and four somatic symptoms (headache, backache, stomachache and dizziness). Socio-economic status was measured using the Family Affluence Scale. Between 1998 and 2018, there were significant increases in the proportion of girls and boys reporting feeling low, feeling nervous, sleep difficulties and backache. Prevalence of the eight individual health complaints was higher among girls and adolescents from lower affluence families. Socio-economic inequalities increased over time, such that declines in mental health were greatest among low affluence adolescents. The data show worsening trends in health complaints among Scottish adolescents between 1998 and 2018, particularly for girls and adolescents from low affluence families. Increasing inequalities in mental health highlight the need to address the underlying social and structural determinants of adolescent mental health.

Physical and mental fatigue in post-COVID syndrome and their associations over time: A small-sample ESM-study to explore fatigue, quality of sleep and behaviours

OBJECTIVE

Post-COVID syndrome leaves millions of people with severe fatigue, yet little is known about its nature in daily life. In this exploratory study, momentary associations between physical and mental fatigue, quality of sleep and behaviours over two weeks in patients with post-COVID syndrome were assessed.

METHOD

Data on fatigue levels, quality of sleep and behaviours was collected for 14 consecutive days using the experience sampling method in ten ex-hospitalised patients with post-COVID syndrome.

RESULTS

Multilevel linear regression modelling showed strong associations between physical and mental fatigue (β = 0.61, p ≤0.001), significant both between and within individuals. Sleeping more hours at night was associated with less physical and mental fatigue the following day (β = -0.35, p = .001; β = -0.27, p = .008). Strenuous relaxation (B = 0.45, p ≤0.001; B = 0.28, p = .004) and social contacts (B = -0.33, p = .003; B = -0.22, p = .02) were associated with physical and mental fatigue at the same measurement point. Performing household chores decreased physical and mental fatigue (B = -0.29, p = .02; B = -0.30, p = .006) two hours later on the same day, whereas eating and drinking increased physical fatigue (B = 0.20, p = .05) two hours later on the same day.

CONCLUSION

Physical fatigue and mental fatigue were strongly associated and revealed fluctuations in fatigue levels between individuals, which might suggest potentially different post-COVID subgroups. Indications for potential risk and beneficial behaviours for fatigue were found.

Exploring the digital footprint of depression: a PRISMA systematic literature review of the empirical evidence

BACKGROUND

This PRISMA systematic literature review examined the use of digital data collection methods (including ecological momentary assessment [EMA], experience sampling method [ESM], digital biomarkers, passive sensing, mobile sensing, ambulatory assessment, and time-series analysis), emphasizing on digital phenotyping (DP) to study depression. DP is defined as the use of digital data to profile health information objectively.

AIMS

Four distinct yet interrelated goals underpin this study: (a) to identify empirical research examining the use of DP to study depression; (b) to describe the different methods and technology employed; (c) to integrate the evidence regarding the efficacy of digital data in the examination, diagnosis, and monitoring of depression and (d) to clarify DP definitions and digital mental health records terminology.

RESULTS

Overall, 118 studies were assessed as eligible. Considering the terms employed, "EMA", "ESM", and "DP" were the most predominant. A variety of DP data sources were reported, including voice, language, keyboard typing kinematics, mobile phone calls and texts, geocoded activity, actigraphy sensor-related recordings (i.e., steps, sleep, circadian rhythm), and self-reported apps' information. Reviewed studies employed subjectively and objectively recorded digital data in combination with interviews and psychometric scales.

CONCLUSIONS

Findings suggest links between a person's digital records and depression. Future research recommendations include (a) deriving consensus regarding the DP definition and (b) expanding the literature to consider a person's broader contextual and developmental circumstances in relation to their digital data/records.

Growing up during a public health crisis: a qualitative study of Born in Bradford early adolescents during Covid-19

BACKGROUND

Whilst children and young people have not often been at forefront of the immediate Covid-19 pandemic health response there has been concern about the indirect consequences of Covid-19 on children's physical and mental health and what the effect of the pandemic will be throughout their lifetimes. Early adolescence is a time of transition and reorientation. This study considers the impact of the first UK Covid-19 lockdown on early adolescents.

METHODS

The study topic was identified through a consultation process which aimed to provide appropriate evidence to local decision makers in Bradford, UK and plan for future interventions. A group of children and their parents from the longitudinal Born in Bradford (BiB) cohort study were randomly selected and then purposively sampled by ethnicity, age, sex and deprivation. The BiB cohort is made up of 13,776 children and their families and were recruited at Bradford Royal Infirmary between 2007 and 2011. 41 interviews (with 20 families: 20 parents and 21 children) were carried out between August and September 2020. Interview data was analysed using reflexive thematic analysis.

RESULTS

The transitional age of the children interviewed had an important influence on their experience Covid-19 and the first UK lockdown. Their age combined with lockdown and school closures meant that they missed out on key learning and social opportunities at a crucial time in their lives. Covid-19 and lockdown also disrupted their daily mental wellbeing and led to increased anxiety, lethargy and low moods, during a period of personal change and social transition.

CONCLUSION

For children at the start of their adolescence undergoing change and formation, the experiences and feelings Covid-19 has set in motion will likely have an impact on their mental and cognitive functioning as they develop further. It is important to acknowledge these early adolescent experiences and continue to monitor and provide targeted support to this group of young people.

A Smartphone Serious Game for Adolescents (Grow It! App): Development, Feasibility, and Acceptance Study

BACKGROUND

Anxiety and mood problems in adolescents often go unnoticed and may therefore remain untreated. Identifying and preventing the development of emotional problems requires monitoring and effective tools to strengthen adolescents' resilience, for example, by enhancing coping skills.

OBJECTIVE

This study describes the developmental process, feasibility, and acceptance of Grow It!, a multiplayer serious game app for adolescents aged 12-25 years. The app consists of the experience sampling method (ESM) to monitor thoughts, behaviors, and emotions in daily life to enhance self-insight and daily cognitive behavioral therapy-based challenges to promote adaptive coping.

METHODS

Our approach entails an iterative game design process combined with an agile method to develop the smartphone app. The incorporated game features (ie, challenges, chat functionality, and visual representation) in the Grow It! app were co-designed with adolescent end users to increase participant engagement and adherence.

RESULTS

The Grow It! app was delivered for Android and iOS in May 2020. Grow It! was offered to adolescents during the COVID-19 crisis between May and December 2020. Participants of the Grow It! COVID-19 study (sample 1: N=685; mean age 16.19, SD 3.11 years; 193/685, 28.2% boys; sample 2: N=1035; mean age 18.78, SD 3.51 years; 193/1035, 18.64% boys) completed 31.5% (13.2/42) to 49.5% (10.4/21) of challenges. Compliance of ESM was suboptimal (35.1/210, 16.7% to 32.5/105, 30.9%). Follow-up questionnaires indicated an overall score of the app of 7.1 out of 10. Moreover, 72.6% (278/383) to 75.6% (487/644) would recommend the app to friends.

CONCLUSIONS

To our knowledge, Grow It! is the first gamified ESM app that both measures individual differences in emotional dynamics and offers an integrated cognitive behavioral therapy-based intervention. Our findings support the feasibility and acceptance, and therefore applicability, of the Grow It! app in adolescents. Further iterations of this serious game app will focus on the increase of compliance and on providing participants feedback through their personal mood profiles.

Tracking Infant Development With a Smartphone: A Practical Guide to the Experience Sampling Method

The COVID-19 pandemic has forced developmental researchers to rethink their traditional research practices. The growing need to study infant development at a distance has shifted our research paradigm to online and digital monitoring of infants and families, using electronic devices, such as smartphones. In this practical guide, we introduce the Experience Sampling Method (ESM) – a research method to collect data, in the moment, on multiple occasions over time – for examining infant development at a distance. ESM is highly suited for assessing dynamic processes of infant development and family dynamics, such as parent-infant interactions and parenting practices. It can also be used to track highly fluctuating family dynamics (e.g., infant and parental mood or behavior) and routines (e.g., activity levels and feeding practices). The aim of the current paper was to provide an overview by explaining what ESM is and for what types of research ESM is best suited. Next, we provide a brief step-by-step guide on how to start and run an ESM study, including preregistration, development of a questionnaire, using wearables and other hardware, planning and design considerations, and examples of possible analysis techniques. Finally, we discuss common pitfalls of ESM research and how to avoid them.

Latent Markov Latent Trait Analysis for Exploring Measurement Model Changes in Intensive Longitudinal Data

Drawing inferences about dynamics of psychological constructs from intensive longitudinal data requires the measurement model (MM)-indicating how items relate to constructs-to be invariant across subjects and time-points. When assessing subjects in their daily life, however, there may be multiple MMs, for instance, because subjects differ in their item interpretation or because the response style of (some) subjects changes over time. The recently proposed "latent Markov factor analysis" (LMFA) evaluates (violations of) measurement invariance by classifying observations into latent "states" according to the MM underlying these observations such that MMs differ between states but are invariant within one state. However, LMFA is limited to normally distributed continuous data and estimates may be inaccurate when applying the method to ordinal data (e.g., from Likert items) with skewed responses or few response categories. To enable researchers and health professionals with ordinal data to evaluate measurement invariance, we present "latent Markov latent trait analysis" (LMLTA), which builds upon LMFA but treats responses as ordinal. Our application shows differences in MMs of adolescents' affective well-being in different social contexts, highlighting the importance of studying measurement invariance for drawing accurate inferences for psychological science and practice and for further understanding dynamics of psychological constructs.