SUNDHED, MENNESKE, KULTUR: – bidrag til sundhedspsykologien

SUNDHED, MENNESKE, KULTUR er et humanistisk forskningsnetværk, der har beskæftiget sig med sundhedsforskning igennem de sidste ni år. En langrække psykologer fra Københavns og Århus Universitet har gennem denne periode været tilknyttet netværket. Gruppen har arbejdet med meget andet end sundhedspsykologi, men har alligevel både direkte og mere indirekte, bidraget til sundhedspsykologiens udvikling. Jeg har i det følgende udvalgt en række temaer fra gruppen arbejde og referencer til enkelte konkrete forskningsprojekter, som enten befinder sig i centrum eller i periferien af det sundhedspsykologiske landskab.In this article 4 research themes are presented as examples of how the humanistic research network HEALTH, HUMANITY, CULTURE has contributed to the development of health research and health psychology. In the article the historical background of health psychology is briefly presented and it is argued that there is a need for interdisciplinaryresearch in order to develop and to provoke the theory and praxis of contemporary health psychology

Sensing behaviour in healthcare design

We are entering an era of distributed healthcare that should fit and respond to individual needs, behaviour and lifestyles. Designing such systems is a challenging task that requires continuous information about human behaviour on a large scale, for which pervasive sensing (e.g. using smartphones and wearables) presents exciting opportunities. While mobile sensing approaches are fuelling research in many areas, their use in engineering design remains limited. In this work, we present a collection of common behavioural measures from literature that can be used for a broad range of applications. We focus specifically on activity and location data that can easily be obtained from smartphones or wearables. We further demonstrate how these are applied in healthcare design using an example from dementia care. Comparing a current and proposed scenario exemplifies how integrating sensor-derived information about user behaviour can support the healthcare design goals of personalisation, adaptability and scalability, while emphasising patient quality of life

Hit in the Heart of Life: How Meeting Like-Minded Peers May Contribute to Psychosocial Recovery of Adolescents and Young Adults With Acquired Brain Injury

Adolescents and young adults are often in a particularly vulnerable position following acquired brain injury (ABI). In addition to neurological and cognitive impairment, they are faced with issues concerning education, job, family, and social life. Moreover, they may be limited in meeting peers and may be left alone with psychosocial issues. This paper investigates how this patient group may benefit from meeting like-minded peers. From information gathered through a questionnaire and interviews with participants in a peer support group, the study aimed to investigate the social and psychological advances such a group can offer, and how this may contribute to psychosocial recovery following ABI. Also, the paper indicates how peer support groups may possibly have an impact on the everyday lives of adolescents and young adults with ABI

Adapting Mobile and Wearable Technology to Provide Support and Monitoring in Rehabilitation for Dementia:Feasibility Case Series

Background: Mobile and wearable devices are increasingly being used to support our everyday lives and track our behavior. Since daily support and behavior tracking are two core components of cognitive rehabilitation, such personal devices could be employed in rehabilitation approaches aimed at improving independence and engagement among people with dementia. Objective: The aim of this work was to investigate the feasibility of using smartphones and smartwatches to augment rehabilitation by providing adaptable, personalized support and objective, continuous measures of mobility and activity behavior. Methods: A feasibility study comprising 6 in-depth case studies was carried out among people with early-stage dementia and their caregivers. Participants used a smartphone and smartwatch for 8 weeks for personalized support and followed goals for quality of life. Data were collected from device sensors and logs, mobile self-reports, assessments, weekly phone calls, and interviews. This data were analyzed to evaluate the utility of sensor data generated by devices used by people with dementia in an everyday life context; this was done to compare objective measures with subjective reports of mobility and activity and to examine technology acceptance focusing on usefulness and health efficacy. Results: Adequate sensor data was generated to reveal behavioral patterns, even for minimal device use. Objective mobility and activity measures reflecting fluctuations in participants’ self-reported behavior, especially when combined, may be advantageous in revealing gradual trends and could provide detailed insights regarding goal attainment ratings. Personalized support benefited all participants to varying degrees by addressing functional, memory, safety, and psychosocial needs. A total of 4 of 6 (67%) participants felt motivated to be active by tracking their step count. One participant described a highly positive impact on mobility, anxiety, mood, and caregiver burden, mainly as a result of navigation support and location-tracking tools. Conclusions: Smartphones and wearables could provide beneficial and pervasive support and monitoring for rehabilitation among people with dementia. These results substantiate the need for further investigation on a larger scale, especially considering the inevitable presence of mobile and wearable technology in our everyday lives for years to come

Development of a Sensor-Based Behavioral Monitoring Solution to Support Dementia Care

Background: Mobile and wearable technology presents exciting opportunities for monitoring behavior using widely available sensor data. This could support clinical research and practice aimed at improving quality of life among the growing number of people with dementia. However, it requires suitable tools for measuring behavior in a natural real-life setting that can be easily implemented by others. Objective: The objectives of this study were to develop and test a set of algorithms for measuring mobility and activity and to describe a technical setup for collecting the sensor data that these algorithms require using off-the-shelf devices. Methods: A mobility measurement module was developed to extract travel trajectories and home location from raw GPS (global positioning system) data and to use this information to calculate a set of spatial, temporal, and count-based mobility metrics. Activity measurement comprises activity bout extraction from recognized activity data and daily step counts. Location, activity, and step count data were collected using smartwatches and mobile phones, relying on open-source resources as far as possible for accessing data from device sensors. The behavioral monitoring solution was evaluated among 5 healthy subjects who simultaneously logged their movements for 1 week. Results: The evaluation showed that the behavioral monitoring solution successfully measures travel trajectories and mobility metrics from location data and extracts multimodal activity bouts during travel between locations. While step count could be used to indicate overall daily activity level, a concern was raised regarding device validity for step count measurement, which was substantially higher from the smartwatches than the mobile phones. Conclusions: This study contributes to clinical research and practice by providing a comprehensive behavioral monitoring solution for use in a real-life setting that can be replicated for a range of applications where knowledge about individual mobility and activity is relevant

Sensing behaviour in healthcare design

We are entering an era of distributed healthcare that should fit and respond to individual needs, behaviour and lifestyles. Designing such systems is a challenging task that requires continuous information about human behaviour on a large scale, for which pervasive sensing (e.g. using smartphones and wearables) presents exciting opportunities. While mobile sensing approaches are fuelling research in many areas, their use in engineering design remains limited. In this work, we present a collection of common behavioural measures from literature that can be used for a broad range of applications. We focus specifically on activity and location data that can easily be obtained from smartphones or wearables. We further demonstrate how these are applied in healthcare design using an example from dementia care. Comparing a current and proposed scenario exemplifies how integrating sensor-derived information about user behaviour can support the healthcare design goals of personalisation, adaptability and scalability, while emphasising patient quality of life

Remember to remember: A feasibility study adapting wearable technology to the needs of people aged 65 and older with Mild Cognitive Impairment (MCI) and Alzheimer's Dementia

Designing for a healthy life includes addressing the needs of an ageing population. The number of people aged 65 and older with mild cognitive impairment and dementia is rising. Whilst there is todate no pharmacological cure, treatments for symptoms and studies into the effect of nonpharmacological interventions have increasingly become available, with the goals of maintaining and supporting cognitive function, helping the person compensate for impairments, and improving the quality of life. Promising yet nascent is the use of wearable technology for cognitive rehabilitation. We conducted an exploratory feasibility study adapting wearable technologies to support the abovementioned elderly user group remember to remember their daily activities such as non-routine appointments. Six design concepts with smartwatches, smart bands, smartphones, smart calendar boards, NFC tags, and augmented reality glasses were sketched and two low-fidelity prototypes, Memofy and Komihu, were developed and tested with three patients and their caregivers. Technology acceptance was high both amongst patients and health personnel, encouraging further in-depth and longitudinal tests for health outcomes

Personal technology use amongst stroke patients : understanding the best platforms for the design of health interventions in treatment and rehabilitation

Europe's healthcare systems are under strain with an ageing population contributing to increased risk of strokes. Rapid technology adaption is needed to prevent, rehabilitate and manage symptoms. This paper identifies what technology platforms are most familiar and accessible to stroke patients to guide designers and engineers to develop future interventions. A survey was distributed to 100 inpatients at a stroke unit, identifying patients' accessibility and usage of personal technologies. Results showed that desktop/laptops and smartphones were most used as opposed to tablets and smartwatches. Different technologies were used for different tasks with a notable lack of devices used for personal health. The underlying reasons for this are discussed with recommendations made on what personal technology platforms should be implemented by designers and engineers in technology-based health interventions

Pervasive assistive technology for people with dementia : a UCD case

Smart mobile and wearable technology offers exciting opportunities to support people with dementia (PwD). Its ubiquity and popularity could even benefit user adoption – a great challenge for assistive technology (AT) for PwD that calls for user-centred design (UCD) methods. This study describes a user-centred approach to developing and testing AT based on off-the-shelf pervasive technologies. A prototype is created by combining a smartphone, smartwatch and various applications to offer six support features. This is tested among five end-users (PwD) and their caregivers. Controlled usability testing was followed by field testing in a real-world context. Data is gathered from video recordings, interaction logs, system usability scale questionnaires, logbooks, application usage logs and interviews structured on the unified theory of acceptance and use of technology model. The data is analysed to evaluate usability, usefulness and user acceptance. Results show some promise for user adoption, but highlight challenges to be overcome, emphasising personalisation and familiarity as key considerations. The complete findings regarding usability issues, usefulness of support features and four identified adoption profiles are used to provide a set of recommendations for practitioners and further research. These contribute toward UCD practices for improved smart, pervasive AT for dementia