In Development

Embrace the formation of social relations in old age.


In addition to the growing prevalence of age-related diseases, horizontal and social networks are shrinking, putting pressure on ageing societies. Research shows that integration into social networks promotes health in old age and that social networks act as moderators of diseases and recovery histories. Numerous studies have shown that friendships as part of the personal network of closer relationships such as family members, friends, and other close confidants mainly contribute to well-being in (late) adulthood.

Friendships are distinct from those fulfilled by family relationships. Whereas instrumental support is a specific family relationship provision – friendships affirm worth and companionship, contributing to social integration in later life. Communication and mutual concern in friendships are associated with higher well-being levels and help alleviate depressive symptoms. Also, friendships are more effective than family relations in preventing loneliness.


ANBO, Carinthia University of Applied Science/ Institute for Applied Research on Ageing (IARA), Seniornett Norge




HannaH – an intelligent matchmaking algorithm that can be operating from a smart speaker.


Tenfolding the opportunity for older adults to stabilise their social convoy, interact directly within a diverse network, and ensure the frequency of social interaction.


Explore how to increase tenfold the opportunity for older adults to stabilise their social convoy.

We aim to disrupt the steadily decreasing social convoy in old age by exploring an intelligent matchmaking algorithm that promotes long-lasting friendships by matching users from a broader social network.

Social convoy theory holds that people maintain a network of social relationships that escorts them over the life course like a convoy, like a group of fellow travellers on the road of life. Relationships in this convoy differ in levels of closeness and dependency on social circumstances and are therefore differently affected by changes in a person’s circumstances. Relationships with people in the innermost circle of the convoy, such as spouses and the core family, should be highly stable throughout the lifespan. Relationships in the periphery of the convoy, such as acquaintances, co-workers, and neighbours, are assumed to be less stable. These relationships may end with external circumstances, such as changes in social roles or location. Various studies provide first support for convoy theory for late adulthood: Close, core relationships remained stable, whereas peripheral relationships decreased in number and contact frequency.

Explore how to increase tenfold the opportunity for older adults to interact directly within a diverse social network.

We aim to explore an intelligent matchmaking algorithm that initiates and facilitates social interactions, directly engaging users within a diverse network to promote well-being and quality of life.

The link between health and social networks has gained attention in the scientific literature over the last two to three decades. Social networks are moderators of disease and recovery histories of ageing-typical diseases such as heart disease, dementia, and cancer. Also, empirical results across populations and different network types show that a complex social network structure enriches well-being; well-being is lowest in older people with small, socially isolated, restricted networks. Although specific structures appear associated with certain functions, we found notable heterogeneity, particularly in friend-focused and limited network types. Diversity in networks of family and friends, on the other hand, goes hand in hand with increased well-being, both mentally and physically.


Explore how to increase tenfold the resilience in social network ecosystems to ensure the frequency of social interaction.

We aim to explore a scalable matchmaking system with existing social network coordinators to ensure the frequency of social interactions in social network ecosystems and support equality and the freedom to choose with whom, for how long and how often individuals want to engage in a social network.

Meta-analysis on age-related differences and changes in social networks shows that the global network increases in adolescence and young adulthood, reaching a plateau in the mid-20s to early 30s and shrinking after that together with the number of named friends. The personal and friendship networks decreased throughout adulthood by almost one person per age decade (average size of friends’ network in the 55+ age group: 4.5). The family network was stable in size from adolescence to old age. Meaning that people manage to keep their family networks relatively stable, especially to the highest age, while friends, for example, are more likely to be lost.

Also, looking beyond the Coronavirus pandemic, we face several forecasted global risk factors like climate change with extreme weather and human-made disasters that can impact our society with natural disasters and infectious diseases. This risk factor is accompanied by research showing that older age in association with chronic illness or multimorbidity and/or unfavourable social, socioeconomic, and environmental conditions are significant risk factors for social isolation and loneliness.


Involving relevant ecosystem stakeholders and primary end-users in co-creating the platform requirements in an iterative fashion, based on their needs.

Learning algorithms are based on experience and become more reliable with a higher number of available training datasets. For individuals with an increased potential for technology anxiety, this may be a barrier to using HannaH. Therefore, together with experts from psychology, social work, and potential users themselves, a robust parameter set must be developed that significantly shortens the learning process of the matchmaking algorithm and thus improves the user experience. In addition, it is essential to collect the appropriate questions for the individual parameters and integrate them into the technical system. In doing so, the economic extremum principle “as little as possible, as much as necessary” is applied not to overtax the users and still execute the algorithm in the best possible way.

Considering this – an essential first step is the co-creation, prototyping, and testing of matchmaking parameters like i.e. topics, emotional temperament, social style, cognitive mode, social skills, communication style, and other individual parameters like socioeconomic status, gender, health, network-disturbing and network-sustaining variables. We are also investigating technology acceptance, ethics, security, UX and an extended business model of the HannaH system.

Throughout the project, we will realise an extensive inclusion of future primary end-users, interdisciplinary experts, and extended ecosystem stakeholders. Our planned co-creation processes mirror the need for interdisciplinary expertise, extended knowledge transfer, and participative user-centred-design based inclusion of primary end-users.

Our workflow is underpinned by principles of iterative user-centred design approaches and ethical guidelines related to good practice in user involvement. At this stage, we engage in theory-driven and expert-based needs & requirement analysis, use case and modular concept analysis, user experience analysis, analysis concerning ethical principles and legal processes, and analysis of extended validation and evaluation scenarios.

Deliverables are matchmaking parameter set and user survey, general requirements, user needs and preferences, and ethical/legal principles.


A social agent adapts to users changing moods and well-being over time and captures matchmaking dimensions relevant to human behaviour and satisfaction.

The use of natural language processing techniques and the output from our co-creation and user involvement processes will serve in a cluster analysis that will train a machine learning (ML) model. The purpose of this model is to identify questions to be used by the matchmaking algorithm. Initially, we will use supervised learning – but aim to build a fully unsupervised matchmaking algorithm capable of producing decision tree questions by itself. Our further aim is to develop a robust training set that improves the matchmaking algorithm from the very first beginning.

A series of breakthroughs in machine learning (ML) has led to a surge in artificial intelligence research progress in the last few years. Despite this progress, ML still has a critical shortcoming: a lack of social intelligence. Personal assistants cannot understand the meaning behind a user’s tone of voice, and recommender systems cannot improve by adapting to the user’s changing mood and wellbeing over time.

The HannaH project is pursuing social and emotional AI to provide the immediate benefit of a system that can collaborate more effectively with users in the matchmaking process. We consider an AI that is intrinsically motivated to produce positive social responses in the humans it interacts with. When a user responds with an angry or frustrated tone, this could act as a negative incentive, training the model not to repeat the action that led to the user’s frustration. Rather than requiring the user to train the device manually, such passive sensing of the user’s emotional state could allow the model to learn quickly and at scale, enabling human-in-the-loop training without extra human effort.

Here is some text from Johannes Johannes Oberzauher, Head of Health and Assistive Technologies Department at the Institute for Applied Research on Ageing, Carinthia University of Applied Science

The HannaH project is currently funded by the innovation hub Buskerud Næringshage in Norway.