Jessica Rees, Research Associate from the Institute of Gerontology at King’s College London, explains more about the use of technology to detect or predict loneliness in older adults.

How can we measure loneliness using technology?

I am—yet what I am none cares or knows” extract of poem by John Clare.

Loneliness, a subjective feeling, which is personal and individual. A general sense of emptiness? Lacking companionship? These are ways psychometric questionnaires might assess loneliness (1). Certain people may resonate with a sense of separateness from others, or a loneliness felt in the presence or absence of a certain relationship (2). Whatever the definition, many older adults experience feelings of loneliness (3). This can lead to poorer health, both physical and mental, higher risk of mortality, decreased wellbeing and more healthcare usage.

When aspects such as person’s age, marital status, health and even pet ownership, combine with life events, such as retirement, moving home or death of a loved one, these lead to a person’s experience of loneliness (4). But what if we could alert a person to their risk of becoming lonely? The use of technology to detect loneliness has the potential to identify when support is needed and improve quality of life for many older adults.

What is the benefit of using technology to detect loneliness?

The benefit of technologies to detect loneliness is that it enables personalisation of measurement. For example, one person may pace more when feeling lonely while another may spend more time in their bedroom. The principle of machine learning highlights that anything with a ‘ground truth’ can be measured. Previous research has used such techniques with information from wearable sensors to detect stress (5). We propose to use a similar approach to identify levels of loneliness in older adults. To date, researchers have used in-home sensors (including cameras) and smartphones (including GPS locations) to measure sleep quality, phone or computer use, activity levels, and time spent outside home to infer loneliness in older adults (6). However, most studies have not addressed important privacy issues in this population which will impact acceptability and use of future technologies (7).

Introducing the DELONELINESS study

The design for healthy ageing: a smart system to decrease loneliness for older people (DELONELINESS) study is being led by researchers at King’s College London, the University of Sussex and the University of Chester. The study aims to develop a smart monitoring and communication system with multifunctional electronics to measure loneliness levels in older people, in collaboration with our industry partner KYMIRA Ltd.

In the first stage of the project, we want to really understand what loneliness means to older adults, and what we need to be able to measure. We are working with the Housing Learning and Improvement Network (LIN)  to recruit a diverse sample of people over the age of 65. By August 2023, we will have interviewed around 60 older adults on their experiences of loneliness and preferences for technology use.

If you are interested in hearing more about the study, please contact: or visit our website.


  1. Maes M, Qualter P, Lodder GMA, Mund M. How (Not) to Measure Loneliness: A Review of the Eight Most Commonly Used Scales. International Journal of Environmental Research and Public Health 2022, Vol 19, Page 10816 [Internet]. 2022 Aug 30 [cited 2022 Sep 13];19(17):10816. Available from:
  2. Mansfield L, Victor C, Meads C, Daykin N, Tomlinson A, Lane J, et al. A conceptual review of loneliness in adults: Qualitative evidence synthesis. Vol. 18, International Journal of Environmental Research and Public Health. MDPI; 2021.
  3. Kitzmüller G, Clancy A, Vaismoradi M, Wegener C, Bondas T. “Trapped in an Empty Waiting Room”—The Existential Human Core of Loneliness in Old Age: A Meta-Synthesis. Qual Health Res. 2018 Jan 1;28(2):213–30.
  4. Campaign to end loneliness. The Psychology of Loneliness Why it matters and what we can do [Internet]. 2020 [cited 2022 Oct 17]. Available from:
  5. Gedam S, Paul S. A Review on Mental Stress Detection Using Wearable Sensors and Machine Learning Techniques. IEEE Access. 2021;9:84045–66.
  6. Latikka R, Rubio-Hernández R, Lohan ES, Rantala J, Nieto Fernández F, Laitinen A, et al. Older Adults’ Loneliness, Social Isolation, and Physical Information and Communication Technology in the Era of Ambient Assisted Living: A Systematic Literature Review. J Med Internet Res 2021;23(12):e28022 [Internet]. 2021 Dec 30 [cited 2022 Sep 29];23(12):e28022. Available from:
  7. Qirtas MM, Zafeiridi E, Pesch D, White EB. Loneliness and Social Isolation Detection Using Passive Sensing Techniques: Scoping Review. JMIR Mhealth Uhealth [Internet]. 2022 Apr 1 [cited 2022 Sep 29];10(4). Available from: /pmc/articles/PMC9044142/