Exploring active ageing in a community-based living environment: an ethnographic study in the Western Norway context.

Background: Age-friendly environments intend to promote active ageing by facilitating social, mental, and physical participation. This could potentially delay the onset of chronic complex conditions, enabling people to live longer independently at home, and prevent loneliness. This study investigates a community-based living environment in Norway called Helgetun and aims to explore how it can facilitate active ageing. Method: We chose an ethnographic approach consisting of observation, informal conversations, and in-depth semi-structured interviews with 15 residents (11 female, 4 male, ages 62-84). We analysed the data using reflexive thematic analysis. Result: We developed three themes on facilitating active ageing in this living environment: maintaining self-identity, experiencing growth and development, and feeling a sense of belonging. These themes were related to physical activity levels, social engagement, and overall satisfaction with the living environment. Maintaining self-identity concerned getting a new role in life as well as access to meaningful activities. Experiencing growth and development involved being exposed to new activities, learning new skills, and experiencing mastery. Lastly, feeling a sense of belonging meant feeling safe and part of a group, as well as receiving social support and help. This feeling of social connectedness and safety was reflected in their experience with the COVID-19 pandemic, in which most felt relatively unaffected, suggesting that this way of living could increase reliance among this age group. Conclusion: Having a flexible structure, adapting to the core needs and individual resources of the residents, can facilitate active ageing in community-based living environments. Our findings contribute to the growing evidence that these environments increase social and physical engagement, whilst reducing social isolation and loneliness. These findings may be particularly relevant in a Norwegian context-where older adults are less dependent on family for care-and are meant as grounding points for policymakers to reflect upon designing future senior living.

Digital phenotyping by wearable-driven artificial intelligence in older adults and people with Parkinson's disease: Protocol of the mixed method, cyclic ActiveAgeing study.

BACKGROUND: Active ageing is described as the process of optimizing health, empowerment, and security to enhance the quality of life in the rapidly growing population of older adults. Meanwhile, multimorbidity and neurological disorders, such as Parkinson's disease (PD), lead to global public health and resource limitations. We introduce a novel user-centered paradigm of ageing based on wearable-driven artificial intelligence (AI) that may harness the autonomy and independence that accompany functional limitation or disability, and possibly elevate life expectancy in older adults and people with PD. METHODS: ActiveAgeing is a 4-year, multicentre, mixed method, cyclic study that combines digital phenotyping via commercial devices (Empatica E4, Fitbit Sense, and Oura Ring) with traditional evaluation (clinical assessment scales, in-depth interviews, and clinical consultations) and includes four types of participants: (1) people with PD and (2) their informal caregiver; (3) healthy older adults from the Helgetun living environment in Norway, and (4) people on the Helgetun waiting list. For the first study, each group will be represented by N = 15 participants to test the data acquisition and to determine the sample size for the second study. To suggest lifestyle changes, modules for human expert-based advice, machine-generated advice, and self-generated advice from accessible data visualization will be designed. Quantitative analysis of physiological data will rely on digital signal processing (DSP) and AI techniques. The clinical assessment scales are the Unified Parkinson's Disease Rating Scale (UPDRS), Montreal Cognitive Assessment (MoCA), Geriatric Depression Scale (GDS), Geriatric Anxiety Inventory (GAI), Apathy Evaluation Scale (AES), and the REM Sleep Behaviour Disorder Screening Questionnaire (RBDSQ). A qualitative inquiry will be carried out with individual and focus group interviews and analysed using a hermeneutic approach including narrative and thematic analysis techniques. DISCUSSION: We hypothesise that digital phenotyping is feasible to explore the ageing process from clinical and lifestyle perspectives including older adults and people with PD. Data is used for clinical decision-making by symptom tracking, predicting symptom evolution, and discovering new outcome measures for clinical trials.

Transcranial Direct Current Stimulation above the Medial Prefrontal Cortex Facilitates Decision-Making following Periods of Low Outcome Controllability.

Recent studies suggest that choice behavior in reinforcement learning tasks is shaped by the level of outcome controllability. In particular, Pavlovian bias (PB) seems to be enhanced under low levels of control, manifesting in approach tendencies toward rewards and response inhibition when facing potential losses. The medial prefrontal cortex (mPFC) has been implicated both in evaluating outcome controllability and in the recruitment of cognitive control (CC) to suppress maladaptive PB during reinforcement learning. The current study tested whether high-definition transcranial direct current stimulation (HD-tDCS) above the mPFC of healthy humans can influence PB, and counteract the previously documented, deleterious behavioral effects of low outcome controllability on decision-making. In a preregistered, between-group, double-blind study (N = 103 adults, both sexes), we tested the interaction between controllability and HD-tDCS on parameters of choice behavior in a Go/NoGo task. Relative to sham stimulation, HD-tDCS resulted in more robust performance improvement following reduced control, an effect that was more pronounced in appetitive trials. In addition, we found evidence for weaker PB when HD-tDCS was administered during low controllability over outcomes. Computational modeling revealed that parameter estimates of learning rate and choice randomness were modulated by controllability, HD-tDCS and their interaction. Overall, these results highlight the potential of our HD-tDCS protocol for interfering with choice arbitration under low levels of control, resulting in more adaptive behavior.