There have been calls in recent years to make data available as a global public good for health and as part of a set of collective actions that are global in scope and are required to address transnational health challenges.
In precision medicine, the focus is on identifying which approaches will be practical for patients based on genetic, environmental, and lifestyle factors. Different daily life resources can be therefore used to assess well-being parameters that can contribute to the level of the intervention acceptance and from which the research on diseases can benefit.
The past years have seen a steep rise in the amount of data being generated from individuals and also used for health purposes. Still, data can’t do the job on its own and it needs to be integrated and properly addressed. With such integration, professionals can efficiently and confidently make informed assessments and if necessary, adjust treatment and care plans.
The outcome is to provide a more personalised level of care for each person. However, some barriers exist. Now, let’s start by remembering the reasons why we want to share the data. We are sharing data because that is essential if we are to provide the very best care we can to the patients. It also enables our stretched healthcare services to work in the most efficient way possible. As discussed in Alzheimer Europe Report, the modern definition of data is “information, especially facts and numbers, collected to be examined, considered and used.” Data originates from the Latin word “datum,” a singular term that means “that which is given.” Therefore, data sharing entails the act of giving: sharing data involves making it available for use by others, for example, investigators or stakeholders.
To support public data altruism we need to understand the common good that comes from it. For example, if we look into the area of dementia research, there is a promise that big data can contribute to its acceleration and give us insight into dementia not only from the disease perspective but also in relation to lifestyles and other factors that can contribute to its manifestation. Quality datasets and good analytical tools can also be used in prevention and disease-management approaches.
To be efficient, different sets of data are needed to develop different approaches. Data relevant to medicine and public health are being generated from a range of sources, including individuals (i.e., through social media and internet-connected devices), public and private health systems, and health researchers. Moreover, we should not forget data from the social care sector. These data are increasingly digitised and critical for the development of new interventions, in particular those that use artificial intelligence and machine learning to improve outcomes.
The relationship between physical health and subjective well-being is, as we will discuss, bidirectional. Older people with illnesses may experience both depression and impaired hedonic and eudemonic (related to happiness) well-being. Well-being might also have a protective role in health maintenance. Therefore, interest in people’s daily lives, their habits, and other day-to-day behaviour is rising in the healthcare sector. Correlations among different parameters (especially connected with wellbeing) create potential opportunities to implement new effective pharmacological, therapeutic, care, and above all preventive interventions.
Today, there is much talk about precision medicine as “an emerging approach for disease treatment and prevention that takes into account individual variability in genes, environment, and lifestyle for each person.” Precision medicine could allow doctors and researchers to predict more accurately the most appropriate treatment and prevention strategies for a particular disease for specific groups of people.
“…the goal of precision medicine is to target the right treatments to the right persons at the right time.”
– FDA on precision medicine
Precision medicine in the context of dementia
Supporting persons with dementia has become the focus of several public health and research programs across the world. Living well with a long-term health condition such as dementia implies the need to monitor well-being within a supportive social environment, despite health-related adversity.
Various studies have shown that non-pharmacological interventions can improve the well-being of people living with dementia. Several studies monitor that aspect with quality of life (QoL) measurement tools as QoL affects the well-being of a person. Well-being is considered as the state of being comfortable, healthy, or happy. The Cambridge Dictionary simplifies the definition of the noun well-being into: “…the state of feeling healthy and happy.”
In simple terms, well-being can be described as judging life positively and feeling good. The National Institutes of Health (NIH) define well-being broadly, relating it to the following in particular: personal dignity (including treatment of the individual with respect), physical and mental health and emotional wellbeing, and protection from abuse and neglect.
There are three main research orientations on well-being: subjective well-being, psychological well-being and social well-being. Subjective well-being mainly refers to an individual’s overall evaluation of their quality of life at a particular stage, and according to the standards set by oneself.
Previous research on people with dementia shows that the concepts of comfort, inclusion, identity, and attachment may be used to characterize the subjective well-being of people with dementia. In one study, six conceptual categories to measure well-being were identified in adults with dementia:
- Positivity: positive emotions that are frequently experienced in the ‘here-and-now setting; including feeling hopefulness, acceptance, and optimism.
- Life purpose: making sense of dementia. Viewpoints shift toward existential implications such as transcendence and spiritual growth.
- A positive sense of self: includes self-worth, identity, and self-efficacy.
- Being active: making active choices to operate ‘normally’ and engaging in meaningful activities that boost positive emotions.
- Healthy relationships: positive features and ‘good connections’ of interpersonal and societal interactions. Attachment and connection, a sense of belonging and safety, as well as feeling appreciated, loved and accepted by others.
- Feeling well: being pleased and satisfied with one’s life as it is.
Research shows that lower levels of behavioral and psychological disturbance (depression, anxiety, disinhibition, and irritability subscales), younger age of the person living with dementia, and worse mental health condition of the caregivers are significantly correlated with lower a quality of life, which are good starting points for designing interventions. A situation that affects the caregiver has a secondary effect on the care process and the person with dementia. That is why we usually talk about QoL and the well-being of people living with dementia, that include the person with the disease, but also those who surround him or her.
Therefore, joint efforts from researchers and care providers, and multiple data sources, including those of the individuals that assume different societal roles are needed to allow precision medicine to become the reality.
Alzheimer Disease’ International (n.d.). Dementia statistics. https://www.alzint.org/about/dementia-facts-figures/dementia-statistics/
Clarke, C., Woods, B., Moniz-Cook, E., Mountain, G., Øksnebjerg, L., Chattat, R., … & Wolverson, E. (2020). Measuring the well-being of people with dementia: a conceptual scoping review. Health and quality of life outcomes, 18(1), 1-14. DOI: 10.1186/s12955-020-01440-x
Hoe, J., Katona, C., Orrell, M., & Livingston, G. (2007). Quality of life in dementia: care recipient and caregiver perceptions of quality of life in dementia: the LASER‐AD study. International Journal of Geriatric Psychiatry: A journal of the psychiatry of late life and allied sciences, 22(10), 1031-1036. DOI: 10.1002/gps.1786
Kaufmann, E. G., & Engel, S. A. (2016). Dementia and well-being: A conceptual framework based on Tom Kitwood’s model of needs. Dementia, 15(4), 774-788. DOI: 10.1177/1471301214539690
Kim, S. (2020). World Health Organization quality of life (WHOQOL) assessment. Encyclopedia of quality of life and well-being research, 1-2.
Lauriks, S., Meiland, F., Osté, J. P., Hertogh, C., & Dröes, R. M. (2020). Effects of assistive home technology on quality of life and falls of people with dementia and job satisfaction of caregivers: Results from a pilot randomized controlled trial. Assistive technology, 32(5), 243-250. DOI: 10.1080/10400435.2018.1531952
Huang D, Wang J, Fang H, Wang X, Zhang Y, Cao S. Global research trends in the subjective well-being of older adults from 2002 to 2021: A bibliometric analysis. Front Psychol. 2022 Sep 9;13:972515. doi: 10.3389/fpsyg.2022.972515. PMID: 36160594; PMCID: PMC9500504.
Freedman VA, Carr D, Cornman JC, Lucas RE. Aging, mobility impairments and subjective wellbeing. Disabil Health J. 2017 Oct;10(4):525-531. doi: 10.1016/j.dhjo.2017.03.011. Epub 2017 Mar 22. PMID: 28385571; PMCID: PMC5610063.
Wahl B, Cossy-Gantner A, Germann S, Schwalbe NR. Artificial intelligence (AI) and global health: how can AI contribute to health in resource-poor settings? BMJ Glob Health. 2018 Aug 29;3(4):e000798. doi: 10.1136/bmjgh-2018-000798. PMID: 30233828; PMCID: PMC6135465.
Schwalbe N, Wahl B. Artificial intelligence and the future of global health. Lancet. 2020 May 16;395(10236):1579-1586. doi: 10.1016/S0140-6736(20)30226-9. PMID: 32416782; PMCID: PMC7255280.
Steptoe A, Deaton A, Stone AA. Subjective wellbeing, health, and ageing. Lancet. 2015 Feb 14;385(9968):640-648. doi: 10.1016/S0140-6736(13)61489-0. Epub 2014 Nov 6. PMID: 25468152; PMCID: PMC4339610.