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The purpose of this paper is to explore the relationship of self-determination with degree of dependence in daily activities among community-dwelling persons aged 80 years and older.

This cross-sectional study focused on community-dwelling people 80 years or older with varied degree of dependence in daily activities. Self-determination in daily life was assessed with the statements from the Impact on Participation and Autonomy-Older persons (IPA-O), and degree of dependence in daily activities was assessed with the activities of daily living (ADL) staircase. Data were analysed using Fisher’s exact test, and the relative risk with a 95 per cent confidence interval was used to explore the risk of perceiving reduced self-determination in daily life.

Compared to the independent persons, the perceived self-determination was significantly lower among persons dependent in instrumental activities of daily living (I-ADL), and persons dependent in personal activities of daily living (P-ADL). Reduced self-determination was most pronounced in persons requiring help with P-ADL.

Following key features could be applied to strengthen the community-dwelling older people’s self-determination; incorporating a dialogue where self-determined questions are raised; adopting a person-centred approach between the persons involved; acknowledging older people’s capabilities – what they are able to do and to be, and what they value.

This study highlights the need of integrating a self-determined dialogue into healthcare where the older person and the professional focus on self-determined questions.

The aim of this chapter is to provide a literature review on daily recovery during non-work time. Specifically, next to discussing theories that help us understand the process of recovery, we will clarify how recovery and its potential outcomes have been conceptualized so far. Consequently, we present empirical findings of diary studies addressing the activities that may facilitate or hinder daily recovery. We will pay special attention to potential mechanisms that may underlie the facilitating or hindering processes. Owing to the limited research on daily recovery, we will review empirical findings on predictors and outcomes of a related construct, namely need for recovery. We conclude with an overall framework from which daily recovery during non-work time can be understood. In this framework, we claim that daily recovery is an important moderator in the process through which job characteristics and their related strain may lead to unfavorable states on a daily basis.

Recognizing daily life activities and human behaviour from contextual information is a challenging task. The purpose of the research work in this paper is to develop a system that can detect indoor and outdoor daily life activities of low entropy mobile people such as elderly people and patients with regular routines using non-intrusive sensor and contextual information.

A framework is proposed that utilises a hierarchical approach in which high-level activities are divided into sub-activities and tasks and recognises the high-level outdoor and indoor activities of daily life. Tasks are recognised at lower level from sensor data and then used by the “activity recogniser” at higher level to recognise the high-level activities. For outdoor activities recognition, wireless proximity data are used, whereas for indoor activities, object usage data obtained through radio frequency identification sensors are used.

For outdoor tasks, results have shown 100 per cent recognition for experiment 1 and a decrease in recognition from 100 to 82.7 per cent, respectively, for experiment 2-9 due to increase in the entropy of individual tasks. Outdoor activity recognition ranges from 84.1 to 100 per cent. For indoor tasks, generating alternative tasks sequences approach effectively recognised the single tasks that were conducted with objects without any order. Average indoor activity recognition rate remains above 90 per cent. The reason why this approach is able to detect the activities without their distinct features is the planning capability of the Asbru that is used in the modelling of high-level activities.

The novelty of this research work is a framework that utilises different types of sensor data and recognises both indoor and outdoor daily life activities of individuals.

The aim of the research work presented in this paper is to investigate a mechanism that can recognise high level activities (for example, going for a walk, travelling on the bus, doing evening activity, etc.) and behaviour of low entropy people (people with regular daily life routines, e.g. elderly people with dementia, patients with regular routines) in order to help them improve their health related daily life activities by using wireless proximity data (e.g. Bluetooth, Wi‐Fi).

The paper adopted a tiered approach to recognise activities and behaviour. Higher level activities are divided into sub‐activities and tasks. Separating the tasks from the raw wireless proximity data is achieved by designing task separator (TASE) algorithm. TASE takes wireless proximity data as an input and separates it into different tasks. These detected tasks and the high level daily activity plans that are made in a planning language Asbru, are then fed into the activity recogniser that compares the detected tasks with the plans and recognises the high level activities that the user is performing.

The paper provides an insight to how only wireless proximity data can be utilised to recognise high level activities and behaviour of individuals. A number of scenarios and experiments are designed to prove the validity of the proposed methodology.

This paper focussed on relatively low entropy individuals with regular routines and behavioural patterns which can be improved by increasing the level of entropies in behavioural routines.

The paper includes implications for the utilisation in health care environments for elderly people and physically impaired individuals.

This paper provides a detailed and original study of algorithms and techniques that can be used to recognise high level activities and behaviour of individuals by using only wireless proximity data.

Pandemic process has led re-questioning home environments within adaptations of new activities inside due to the restriction of outdoor usage. This paper aims to reveal spatial transformations carried out through the time spent at home during the COVID-19 pandemic.

A qualitative study is carried out based on Garfinkel’s (1961) ethnomethodology method (documentary method of interpretation). Analysis focused on the first month of pandemic in which urgent adaptations and solutions produced at home, and the situation in last two weeks of pandemic is analyzed within a control group and comparisons were made. Perec’s (1974) statement of “a user's manual” is used as a checklist of time-space-activity observations proposed on “mondayness, tuesdayness, wednesdayness, thursdayness, fridayness, saturdayness and sundayness.” Instead of daily period, activities performed on a weekly scale were recorded and time-dependent shifts of functions were revealed.

Findings introduced new discussion topics to examine dwelling spaces in “new normal.” Comparisons indicate that hypothesis of gathering all activities in room in terms of “life fits into room” concept is directed through rapid spatial behaviors in daily life as primary adaptation.

Although pandemic process continues after study, analysis of first four weeks are accepted as the period of rapid change and compared by last two weeks of pandemic.

Pandemic indicators proposed in study brings along that there is little information on the subject in literature. Socio-spatial findings address the examinations of spatial transformations into pandemic adaptation.

Exercising is a key approach adopted by muscular dystrophy patients to halt the weakening of muscles as it can eventually lead to serious immobility issues. Though it is essential to exercise on a daily basis for healthy living, there is no mention of any research effort in the current literature regarding the development of an apparel product for these mobility-affected patients that might assist them both in meeting their exercising needs and providing them some comfort in their daily living. Thus, this paper aims to focus on identifying the specific needs of muscular dystrophy victims and proposing special adaptive clothing solutions to support their daily exercise and mobility needs.

To achieve the objectives of this study, attention was focused on the muscular dystrophy afflicted women in Sri Lanka. Semi-structured interviews were conducted with the female victims of muscular dystrophy and their lifestyles were observed carefully; additional data were gathered by holding semi-structured interviews with their physiotherapists. Further, interviews were conducted with both garment technologists and fabric technologists too. Data gathered through these methods were analyzed qualitatively using the principles of thematic analysis and then aggregate conclusions were drawn.

It was observed that the patients were engaged in special activities such as exercising three times a day besides following their normal day-to-day activities to maintain and develop muscle strength. It soon became evident that these women found it difficult to perform their daily exercise routines with their regular clothing and were looking for custom made clothing they could wear all day long in comfort and avoid the problems that arose while exercising. The study specifies the requirements that must be met to satisfy both generic and specific needs. Considering all these aspects some adaptive clothing solutions were proposed to support daily exercising activity with respect to comfort, convenience, health and safety, as well as socio-cultural and psychological needs.

The area of fusing generic and specific features to support the daily exercising needs of muscular dystrophy victims is an untouched field of experimentation and being a need of the disabled, the present study marks a milestone on the way to a novel area of apparel design, besides exploring a new field of research.

In sensor-based activity recognition, most of the previous studies focused on single activities such as body posture, ambulation and simple daily activities. Few works have been done to analyze complex concurrent activities. The purpose of this paper is to use a statistical modeling approach to classify them.

In this study, the recognition problem of concurrent activities is explored with the framework of parallel hidden Markov model (PHMM), where two basic HMMs are used to model the upper limb movements and lower limb states, respectively. Statistical time-domain and frequency-domain features are extracted, and then processed by the principal component analysis method for classification. To recognize specific concurrent activities, PHMM merges the information (by combining probabilities) from both channels to make the final decision.

Four studies are investigated to validate the effectiveness of the proposed method. The results show that PHMM can classify 12 daily concurrent activities with an average recognition rate of 93.2 per cent, which is superior to regular HMM and several single-frame classification approaches.

A statistical modeling approach based on PHMM is investigated, and it proved to be effective in concurrent activity recognition. This might provide more accurate feedback on people’s behaviors.

The research may be significant in the field of pervasive healthcare, supporting a variety of practical applications such as elderly care, ambient assisted living and remote monitoring.

The authors are in the process of exploring an information support robot to support daily activities of people with mild cognitive impairment or dementia. The purpose of this paper is to reveal how the robot should talk to an older woman with dementia to make her perform daily activities.

The robot asked to the participant to do some daily activities; Task A, taking medicine; Task B, measuring blood pressure; and Task C, cleaning up the room in three different ways for each task. In the talking pattern 1 (TP1), the robot simply informed what the tasks were. The talking patterns 2 and 3 (TP2 and TP3) were separated according to the process of activities in two and three steps, respectively. The participant was required to answer “Yes” if she understood what the robot talked to her, and perform the tasks.

The participant was not able to prepare water in the Task A when the robot spoke the TP1 (performance rate (PR) was 71.4 per cent). However, she could perfectly take medicine in the case when the robot spoke the processes of the task by the TP3 (PR was 100.0 per cent). The similar tendencies were observed in the Tasks B and C.

Multicenter studies would be required to apply these findings to a larger population.

The authors confirmed that it might be important to determine how the robot talked to people with dementia to properly facilitate their daily activities.

How people spend their time is an indicator of how they live their lives, with time use over the life course conditioned both by age and by participation in age-graded institutions. This chapter uses nationally representative data from the pooled 2008–2020 American Time Use Survey (ATUS) to evaluate how time use in 12 activity categories varies by age, gender, and disability status among 137,266 respondents aged 15 and older. By doing so, we quantify the “disability gap” in time use between men and women with and without disabilities, identifying at what age and by how much people with disabilities experience time differentials in activities of daily living (ADLs), instrumental activities of daily living (IADLs), and other indicators of social participation. Results indicate that – at many ages – patterns of time use for people with disabilities deviate from those of people without disabilities, with more pronounced differences in midlife. Further, the magnitude of women's disability gaps equals or exceeds men's for sleeping, and nearly all ADLs and IADLs, indicating that disability gaps are also gendered.

Substance use disorders (SUDs) often co-occur with major depressive disorder (MDD), yet treatments targeting this comorbidity are limited, especially in resource limited settings where individuals with SUDs often receive treatment.

Based upon principles of reinforcement and behavioral economic models of substance use, as well as the Brief Behavioral Activation Treatment for Depression (BATD; Lejuez et al., 2011), the Life Enhancement Treatment for Substance Use (LETS ACT; Daughters et al., 2008) was developed to meet the unique needs of patients with MDD-SUD comorbidity.

The current manuscript presents a summary of the theoretical foundations and key components of LETS ACT.

A specific focus on increasing dissemination via the flexible delivery dependent on patient and treatment setting characteristics is provided throughout.