Sensor-based remote health monitoring can be used for prompt detection of adverse health in people living with dementia in the home. Current anomaly detection approaches are challenged by noisy data, unreliable event annotation and wide variability in home settings. We hypothesized that a downturn in health would present as a discernible shift in spatiotemporal patterns, which can be identified by monitoring the temporal evolution of the household movement graph. We present a lightweight contrastive learning approach to detect adverse events using home activity changes, along with household-personalized alerting thresholds based on the clinician-set target alert rate. Our self-supervised Graph Barlow Twins model with aggregation-based node feature masking is used to generate daily activity representations in participant households taken from a real-world dataset collected by the UK Dementia Research Institute. Daily graph differences represent the anomaly score, which are compared to the householdpersonalized threshold, and alerts raised to the clinical monitoring team. Attention weights from the graph encoder support explainability and help focus on the source of anomaly. Our model outperforms state-of-the-art temporal graph algorithms in detecting agitation and fall events for three distinct patient cohorts, with 81% average recall and 88% generalizability at a target alert rate of 7%. To the best of our knowledge, we offer the first use case of negative sample-free graph contrastive learning for anomaly detection in a healthcare setting that is domain-agnostic and can be applied to wider IoT settings.