From Measuring to Protecting “Days at Home”: Toward Frailty‐Informed Monitoring After Serious Health Events

We read with great interest the recent article by Gill and colleagues describing “days at home” as a patient-centered outcome following serious health events among community-living older persons [1]. By integrating survival, institutionalization, and hospitalization into a single measure, the authors illuminate an outcome that closely reflects what older adults consistently value: remaining alive, independent, and within the familiarity of their own homes. In doing so, the study subtly reframes recovery after acute illness not merely as survival, but as the preservation of life within its social and functional context. Because the ability to remain at home reflects autonomy, social participation, and quality of life, “days at home” has emerged as a particularly meaningful outcome in geriatric research. Beyond its intuitive appeal, “days at home” (or “home time”) has been increasingly proposed as a candidate universal quality-of-life measure for older adults [2]. Recent consensus efforts have attempted to standardize its definition as time alive and spent outside healthcare institutions, integrating multiple dimensions of health status into a single, patient-centered metric [2]. However, important areas of uncertainty remain regarding which settings to include, how to weight different forms of healthcare utilization, and the optimal timeframe for measurement. These unresolved methodological questions underscore how difficult it is to operationalize a seemingly simple concept. The meaning and interpretation of “days at home” may vary substantially across clinical contexts, care systems, and patient trajectories, challenging the feasibility of a truly universal measure [3]. These limitations suggest that “days at home” should not be viewed solely as a retrospective summary measure, but rather as a dynamic and context-dependent outcome that may benefit from continuous observation and interpretation. Framed in this way, an important question emerges: should healthcare systems remain satisfied with measuring the loss of “days at home” retrospectively, or should we begin to actively safeguard those days? Following acute illness, or non-elective surgery, many older adults enter a state of frailty marked by reduced physiological reserve and heightened vulnerability to even minor stressors (e.g., infection, medication imbalance, sleep disruption or declining mobility) that can rapidly precipitate clinical deterioration [4]. Within this vulnerable phase, the trajectory toward loss of days at home often unfolds gradually, through subtle and initially reversible changes. Traditional follow up models, largely based on intermittent clinical encounters, provide only brief glimpses into this unstable recovery phase and rarely capture the subtle physiological and behavioral signals that precede decline. In this context, recent advances in wearable sensing technologies offer an opportunity to extend the observation of “days at home” beyond static measurement. Modern wearable devices can capture real-world physiological and behavioral signals (e.g., including activity levels, mobility patterns, sleep characteristics, and heart rate variability) within the patient's daily environment. Emerging evidence suggests that passive monitoring of these signals may detect early changes in functional status before overt clinical deterioration or hospital admission [5], positioning digital biomarkers as potential indicators of evolving health trajectories [5]. However, the large volume of continuous physiological and behavioral data generated by these systems requires analytical approaches capable of identifying meaningful patterns. When interpreted within a clinically grounded framework, these data may help characterize the evolving trajectories that precede the loss of days at home. Machine learning approaches, applied to multimodal physiological and behavioral datasets, may support the early identification of subtle deviations associated with functional decline or hospitalization [6]. In this sense, digital monitoring systems may enable a shift from reactive care to anticipatory strategies aimed at preserving stability and maintaining time spent at home. Importantly, the integration of artificial intelligence into clinical medicine must remain firmly grounded in human judgment, requiring careful balance between technological opportunity, methodological rigor, and clinical responsibility [7]. Within such a human-supervised framework, continuous monitoring technologies could function as early detection systems embedded within everyday care. Subtle signals of instability (e.g., declining mobility, fragmented sleep, reduced activity or altered physiological variability) might trigger timely reassessment and intervention, including medication review, telemedicine evaluation, reinforcement of rehabilitation programs or targeted home support [8], helping prevent avoidable hospitalizations and preserve functional independence. Seen through this lens, the concept of “days at home”, as highlighted by Gill and colleagues and further discussed in recent efforts to define “home time,” may evolve from a descriptive outcome measure into a guiding principle for post-acute geriatric care. Rather than waiting for deterioration to occur, healthcare systems could adopt a frailty-informed model focused on continuous observation and early stabilization. The aim would not simply be to rescue patients once they fall back into crisis, but to preserve them within their fragile equilibrium and, when possible, gradually restore resilience. One might therefore view the frail older adult as navigating an unstable recovery pathway, where continuous monitoring, supported by predictive analytics and guided by clinical oversight, functions as a “lighthouse,” identifying early deviations and enabling timely intervention (Figure 1). Gill and colleagues have shown how many days older adults lose outside their homes after serious illness. The next challenge for geriatric medicine may therefore be to move beyond measuring these days toward actively protecting them. Ultimately, the true measure of success may lie not in how effectively we rescue frail patients once deterioration occurs, but in how rarely they reach that point at all. E.G.B.: conceptualization, supervision, writing – review and editing. M.M., S.F.: writing – original draft, literature review. C.S., N.C., V.B.: Writing – review and editing, critical revision. T.M.: supervision, critical revision of content. All authors have read and approved the final manuscript. The corresponding author affirms that all individuals who contributed significantly to this work have been listed. The corresponding author also confirms that all coauthors have been notified of the manuscript submission to JAGS, all changes in revised versions, and the final editorial decision. The spelling of authors' names, order of authorship, and affiliations have been verified for accuracy. The authors have nothing to report. The authors declare no conflicts of interest. This publication is linked to a related reply article by Thomas M. Gill. To view this article, visit https://doi.org/10.1111/jgs.70511.