JMIR Publications

ABSTRACT

Background:

Quality of life (QoL) plays a crucial role in dementia care, yet QoL and its dynamic, context-dependent nature can be difficult to capture in people living with dementia due to challenges in memory and communication and limitations of self-reported QoL instruments. Observational tools such as the Maastricht Electronic Daily Life Observation (MEDLO) provide narrative descriptions of the daily life of people living with dementia in nursing homes. However, the MEDLO tool was not developed to assess QoL specifically, and it remains unclear to what extent its narrative descriptions reflect aspects of QoL. Analysing these narrative descriptions is labour-intensive and time-consuming. Recent advances in natural language processing (NLP), including Large Language Models, offer potential to analyse these narrative descriptions at scale.

Objective:

The study aims to gain insight into the QoL in people living with dementia residing in nursing homes in the Netherlands, using NLP to interpret narratives of daily life in existing MEDLO data.

Methods:

This study conducted a secondary analysis of existing MEDLO observational data from 151 people living with dementia residing in Dutch long-term care. Narrative data had been documented by trained observers, describing activities, interactions, settings and emotional expressions. For analysis, a local secure pipeline was developed in which GPT-4o-mini was deployed for NLP tasks. The pipeline comprised three analytical steps: (1) N-gram frequency analysis to identify common language patterns, (2) sentiment analysis of positive and negative expressions per QoL domains, and (3) topic modelling to group semantically related terms and map them to QoL domains. Outputs were iteratively refined through prompt engineering and validated through expert review for coherence and contextual relevance.

Results:

A total of 5,622 narratives (50,106 words) from 151 observed people living with dementia were analysed. The narratives were short, averaging 8.5 words per narrative. N-gram frequency analysis identified frequent documentation of passive activity (sits at the table) in limited indoor settings (living room). Emotional well-being was often described in positive terms (smiles, laughs), whereas explicitly negative expressions (cries, distress) occurred less frequently. Weighted sentiment analysis showed that, although fewer in number, negative expressions carried a stronger intensity, resulting in an overall predominance of negative sentiment across all QoL domains. Topic modelling identified eight coherent clusters, most of which mapped onto multiple QoL domains, underscoring QoL’s multidimensionality.

Conclusions:

NLP identified predominantly passive activities in little varying indoor settings, yet people living with dementia were often described with positive affect, underscoring both the complexity of QoL in dementia and the influence of documentation practices. In practice, NLP could help translate everyday care documentation into actionable information that guides more responsive, person-centred dementia care.