Less inhibited and more depressed? The puzzling association between mood, inhibitory control and depressive symptoms

Yitzhak, Neta; Shimony, Orly; Oved, Nisiel; Bonne, Omer; Nahum, Mor

doi:10.1016/j.comppsych.2023.152386

Cited by 4 publications

(4 citation statements)

References 59 publications

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Section: Discussionmentioning

confidence: 99%

“…However, the HAPA model follows a linear pattern from intention to post-intentional processes to predict the likelihood of successful change. Depressed individuals have great variability in mood (Yitzhak et al, 2023) making it less likely that a linear model of change will predict positive action following intentions (Hayes and Andrews, 2020). A long-established theory that recognizes the unpredictability of dynamic systems, known as 'Chaos Theory', may help shed further light on the challenges of using a linear health behaviour model.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Exploring behaviour-intention gaps to explain low retention in a community-based video-conferencing intervention for depression: lessons for researchers using technology as interventions

Booth,

McConnell,

Tully

et al. 2024

MHDT

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Purpose This paper aims to reflect on the outcomes of a community-based video-conferencing intervention for depression, predating the COVID-19 pandemic. The study investigates the potential implications of its findings for enhancing adherence to digital mental health interventions. The primary objective is to present considerations for researchers aimed at minimising the intention-behaviour gap frequently encountered in digital mental health interventions. Design/methodology/approach A randomised control feasibility trial design was used to implement a telehealth model adapted from an established face-to-face community-based intervention for individuals clinically diagnosed with depression. In total, 60 participants were initially recruited in association with a local mental health charity offering traditional talking-based therapies with only eight opting to continue through all phases of the project. Modifications aligning with technological advancements were introduced. Findings However, the study faced challenges, with low uptake observed after an initial surge in recruitment interest. The behaviour-intention gap highlighted technology as a barrier to service accessibility, exacerbated by participant age. Furthermore, the clinical diagnosis of depression, characterised by low mood and reduced interest in activities, emerged as a potential influencing factor. Research limitations/implications The limitations of the research include its pre-pandemic execution, during a nascent stage of technological mental health interventions when participants were less familiar with online developments. Practical implications Despite these limitations, this study's reflections offer valuable insights for researchers aiming to design and implement telehealth services. Addressing the intention-behaviour gap necessitates a nuanced understanding of participant demographics, diagnosis and technological familiarity. Social implications The study's relevance extends to post-pandemic society, urging researchers to reassess assumptions about technology availability to ensure engagement. This paper contributes to the mental health research landscape by raising awareness of critical considerations in the design and implementation of digital mental health interventions. Originality/value Reflections from a pre-pandemic intervention in line with the developments of a post-pandemic society will allow for research to consider that because the technology is available does not necessarily result in engagement.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Exploring behaviour-intention gaps to explain low retention in a community-based video-conferencing intervention for depression: lessons for researchers using technology as interventions

Booth,

McConnell,

Tully

et al. 2024

MHDT

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“…The use of a Likert scale for the responses in the study was chosen because we did not want to burden the user with lengthy symptom surveys at each session for up to 60 sessions. Also, Likert scales have been standardly applied for longitudinal EMA-based mood/behavior monitoring in past research [ 34 , 35 , 36 , 37 , 38 , 39 ].…”

Section: Methodsmentioning

confidence: 99%

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals

Nan,

Herbert,

Purpura

et al. 2024

Sensors

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Healthcare professionals are known to suffer from workplace stress and burnout, which can negatively affect their empathy for patients and quality of care. While existing research has identified factors associated with wellbeing and empathy in healthcare professionals, these efforts are typically focused on the group level, ignoring potentially important individual differences and implications for individualized intervention approaches. In the current study, we implemented N-of-1 personalized machine learning (PML) to predict wellbeing and empathy in healthcare professionals at the individual level, leveraging ecological momentary assessments (EMAs) and smartwatch wearable data. A total of 47 mood and lifestyle feature variables (relating to sleep, diet, exercise, and social connections) were collected daily for up to three months followed by applying eight supervised machine learning (ML) models in a PML pipeline to predict wellbeing and empathy separately. Predictive insight into the model architecture was obtained using Shapley statistics for each of the best-fit personalized models, ranking the importance of each feature for each participant. The best-fit model and top features varied across participants, with anxious mood (13/19) and depressed mood (10/19) being the top predictors in most models. Social connection was a top predictor for wellbeing in 9/12 participants but not for empathy models (1/7). Additionally, empathy and wellbeing were the top predictors of each other in 64% of cases. These findings highlight shared and individual features of wellbeing and empathy in healthcare professionals and suggest that a one-size-fits-all approach to addressing modifiable factors to improve wellbeing and empathy will likely be suboptimal. In the future, such personalized models may serve as actionable insights for healthcare professionals that lead to increased wellness and quality of patient care.

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“…Inhibitory control is closely related to fluid intelligence, and enhancing inhibitory control can improve fluid intelligence [9,10]. Depression [11,12], obsessive-compulsive disorder [13,14], obesity [15,16], addictive habits [17,18], etc., are also closely related to impaired inhibitory control. In conclusion, inhibitory control, as an essential cognitive function, is particularly important for individual healthy development.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Rhythmic Audio-Visual Stimulation on Inhibitory Control: An ERP Study

Wang,

Wu,

Sun

et al. 2024

Brain Sciences

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Inhibitory control, as an essential cognitive ability, affects the development of higher cognitive functions. Rhythmic perceptual stimulation has been used to improve cognitive abilities. It is unclear, however, whether it can be used to improve inhibitory control. This study used the Go/NoGo task and the Stroop task to assess various levels of inhibitory control using rhythmic audio-visual stimuli as the stimulus mode. Sixty subjects were randomly divided into three groups to receive 6 Hz, 10 Hz, and white noise stimulation for 30 min. Two tasks were completed by each subject both before and after the stimulus. Before and after the task, closed-eye resting EEG data were collected. The results showed no differences in behavioral and EEG measures of the Go/NoGo task among the three groups. While both 6 Hz and 10 Hz audio-visual stimulation reduced the conflict effect in the Stroop task, only 6 Hz audio-visual stimulation improved the amplitude of the N2 component and decreased the conflict score. Although rhythmic audio-visual stimulation did not enhance response inhibition, it improved conflict inhibition.

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Less inhibited and more depressed? The puzzling association between mood, inhibitory control and depressive symptoms

Cited by 4 publications

References 59 publications

Exploring behaviour-intention gaps to explain low retention in a community-based video-conferencing intervention for depression: lessons for researchers using technology as interventions

Exploring behaviour-intention gaps to explain low retention in a community-based video-conferencing intervention for depression: lessons for researchers using technology as interventions

Personalized Machine Learning-Based Prediction of Wellbeing and Empathy in Healthcare Professionals

The Effect of Rhythmic Audio-Visual Stimulation on Inhibitory Control: An ERP Study

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