Delivering Digital Healthcare for Elderly: A Holistic Framework for the Adoption of Ambient Assisted Living.

Adoption of Ambient Assisted Living (AAL) technologies for geriatric healthcare is suboptimal. This study aims to present the AAL Adoption Diamond Framework, encompassing a set of key enablers/barriers as factors, and describe our approach to developing this framework. A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. SCOPUS, IEEE Xplore, PubMed, ProQuest, Science Direct, ACM Digital Library, SpringerLink, Wiley Online Library and grey literature were searched. Thematic analysis was performed to identify factors reported or perceived to be important for adopting AAL technologies. Of 3717 studies initially retrieved, 109 were thoroughly screened and 52 met our inclusion criteria. Nineteen unique technology adoption factors were identified. The most common factor was privacy (50%) whereas data accuracy and affordability were the least common factors (4%). The highest number of factors found per a given study was eleven whereas the average number of factors across all studies included in our sample was four (mean = 3.9). We formed an AAL technology adoption framework based on the retrieved information and named it the AAL Adoption Diamond Framework. This holistic framework was formed by organising the identified technology adoption factors into four key dimensions: Human, Technology, Business, and Organisation. To conclude, the AAL Adoption Diamond Framework is holistic in term of recognizing key factors for the adoption of AAL technologies, and novel and unmatched in term of structuring them into four overarching themes or dimensions, bringing together the individual and the systemic factors evolving around the adoption of AAL technology. This framework is useful for stakeholders (e.g., decision-makers, healthcare providers, and caregivers) to adopt and implement AAL technologies.

Development and Validation of a Taxonomy for Characterizing Measurements in Health Self-Quantification.

BACKGROUND: The use of wearable tools for health self-quantification (SQ) introduces new ways of thinking about one's body and about how to achieve desired health outcomes. Measurements from individuals, such as heart rate, respiratory volume, skin temperature, sleep, mood, blood pressure, food consumed, and quality of surrounding air can be acquired, quantified, and aggregated in a holistic way that has never been possible before. However, health SQ still lacks a formal common language or taxonomy for describing these kinds of measurements. Establishing such taxonomy is important because it would enable systematic investigations that are needed to advance in the use of wearable tools in health self-care. For a start, a taxonomy would help to improve the accuracy of database searching when doing systematic reviews and meta-analyses in this field. Overall, more systematic research would contribute to build evidence of sufficient quality to determine whether and how health SQ is a worthwhile health care paradigm. OBJECTIVE: The aim of this study was to investigate a sample of SQ tools and services to build and test a taxonomy of measurements in health SQ, titled: the classification of data and activity in self-quantification systems (CDA-SQS). METHODS: Eight health SQ tools and services were selected to be examined: Zeo Sleep Manager, Fitbit Ultra, Fitlinxx Actipressure, MoodPanda, iBGStar, Sensaris Senspod, 23andMe, and uBiome. An open coding analytical approach was used to find all the themes related to the research aim. RESULTS: This study distinguished three types of measurements in health SQ: body structures and functions, body actions and activities, and around the body. CONCLUSIONS: The CDA-SQS classification should be applicable to align health SQ measurement data from people with many different health objectives, health states, and health conditions. CDA-SQS is a critical contribution to a much more consistent way of studying health SQ.

Refining the Concepts of Self-quantification Needed for Health Self-management. A Thematic Literature Review.

BACKGROUND: Questions like 'How is your health? How are you feeling? How have you been?' now can be answered in a different way due to innovative health self-quantification apps and devices. These apps and devices generate data that enable individuals to be informed and more responsible about their own health. OBJECTIVES: The aim of this paper is to review studies on health SQ, firstly, exploring the concepts that are associated with the users' interaction with and around data for managing health; and secondly, the potential benefits and challenges that are associated with the use of such data to maintain or promote health, as well as their impact on the users' certainty or confidence in taking effective actions upon such data. METHODS: To answer these questions, we conducted a comprehensive literature review to build our study sample. We searched a number of electronic bibliographic databases including Scopus, Web of Science, Medline, and Google Scholar. Thematic analysis was conducted for each study to find all the themes that are related to our research aims. RESULTS: In the reviewed literature, conceptualisation of health SQ is messy and inconsistent. Personal tracking, personal analytics, personal experimentation, and personal health activation are different concepts within the practice of health SQ; thus, a new definition and structure is proposed to set out boundaries between them. Using the data that are generated by SQS for managing health has many advantages but also poses many challenges. CONCLUSIONS: Inconsistency in conceptualisation of health SQ - as well as the challenges that users experience in health self-management - reveal the need for frameworks that can describe the users' health SQ practice in a holistic and consistent manner. Our ongoing work toward developing these frameworks will help researchers in this domain to gain better understanding of this practice, and will enable more systematic investigations which are needed to improve the use of SQS and their data in health self-management.