Quantifying resilience of humans and other animals.

All life requires the capacity to recover from challenges that are as inevitable as they are unpredictable. Understanding this resilience is essential for managing the health of humans and their livestock. It has long been difficult to quantify resilience directly, forcing practitioners to rely on indirect static indicators of health. However, measurements from wearable electronics and other sources now allow us to analyze the dynamics of physiology and behavior with unsurpassed resolution. The resulting flood of data coincides with the emergence of novel analytical tools for estimating resilience from the pattern of microrecoveries observed in natural time series. Such dynamic indicators of resilience may be used to monitor the risk of systemic failure across systems ranging from organs to entire organisms. These tools invite a fundamental rethinking of our approach to the adaptive management of health and resilience.

What matters in "multimorbidity"? Arguably resilience and personal health experience are central to quality of life and optimizing survival.

RATIONALE: Much is written about "multimorbidity" as it is a difficult problem for health systems, as it reflects a complex phenomenon unique to each individual health journey and health service context. This paper proposes the adoption of 2 constructs or knowledge streams into mainstream "multimorbidity" care which are arguably most important to person-centered care-personal health perceptions and resilience. ANALYSIS: "Multimorbidity" is the manifestation of multiple nonlinear physical, psychosocial, and environmental phenomena in an individual health journey. Multimorbidity encompasses very stable states for the most part together with highly unstable phases that are difficult to manage. Averting or controlling the underlying loss of resilience in instability can be challenging without early warning signals pointing towards tipping points. Monitoring resilience and early warning signals for tipping points is new to health care. Yet what should we monitor in the complexity of multimorbidity? There are multiple and competing health service features and biometrics that can be measured. However, an expanding of literature endorses importance of simply asking a person about their self-rated health in order to provide predictions of their resilience and survival. Interoception, exemplified as self-rated health, arises from internal neurocognitive self-monitoring functions of different internal and external phenomena. Interoception is being to be recognized as predictors and barometers of resilience and survival. CONCLUSIONS: Two phenomena of human systems-interoception and resilience-can guide care in the complex nature of multimorbidity in unstable health journeys and should be incorporated into clinical practice.

Resilience and health (care): A dynamic adaptive perspective.

This special forum on resilience explores particular worldviews of resilience-clinical, psychosocial, sociological, complexity science, organizational, and political economy through eight papers. This forum aims to open up the wealth of understandings and implications in health care by taking a transdisciplinary overview.

Health sciences research and Aboriginal communities: pathway or pitfall?

OBJECTIVE: To provide health researchers and clinicians with background information and examples regarding Aboriginal health research challenges, in an effort to promote effective collaborative research with Aboriginal communities. METHODS: An interdisciplinary team of experienced Aboriginal-health researchers conducted a thematic analysis of their planning meetings regarding a community-based Aboriginal health research training project and of the text generated by the meetings and supplemented the analysis with a literature review. RESULTS: Four research challenges are identified and addressed: (1) contrasting frameworks of Western science and indigenous knowledge systems; (2) the impact of historic colonialist processes upon the interface between health science research and Aboriginal communities; (3) culturally relevant frameworks and processes for knowledge generation and knowledge transfer; and (4) Aboriginal leadership, governance, and participation. CONCLUSION: Culturally appropriate and community-controlled collaborative research can result in improved health outcomes in Aboriginal communities and contribute new insights and perspectives to the fields of public health and medicine in general.

Knowledge translation and indigenous knowledge.

OBJECTIVE: We wanted to evaluate the interface between knowledge translation theory and Indigenous knowledge. DESIGN: Literature review supplemented by expert opinion was carried out. METHOD: Thematic analysis to identify gaps and convergences between the two domains was done. RESULTS: The theoretical and epistemological frameworks underlying Western scientific and Indigenous knowledge systems were shown to have fundamental differences. CONCLUSION: Knowledge translation methods for health sciences research need to be specifically developed and evaluated within the context of Aboriginal communities.

Self-rated health: patterns in the journeys of patients with multi-morbidity and frailty.

RATIONALE, AIMS AND OBJECTIVES: Self-rated health (SRH) is a single measure predictor of hospital utilization and health outcomes in epidemiological studies. There have been few studies of SRH in patient journeys in clinical settings. Reduced resilience to stressors, reflected by SRH, exposes older people (complex systems) to the risk of hospitalization. It is proposed that SRH reflects rather than predicts deteriorations and hospital use; with low SRH autocorrelation in time series. The aim was to investigate SRH fluctuations in regular outbound telephone calls (average biweekly) to patients by Care Guides. METHODS: Descriptive case study using quantitative autoregressive techniques and qualitative case analysis on SRH time series. Fourteen participants were randomly selected from the Patient Journey Record System (PaJR) database. The PaJR database recorded 198 consecutively sampled older multi-morbid patients journeys in three primary care settings. Analysis consisted of triangulation of SRH (0 very poor - 6 excellent) patterns from three analyses: SRH graduations associations with service utilization; time series modelling (autocorrelation, and step ahead forecast); and qualitative categorization of deteriorations. RESULTS: Fourteen patients reported mean SRH 2.84 (poor-fair) in 818 calls over 13 ± 6.4 months of follow-up. In 24% calls, SRH was poor-fair and significantly associated with hospital use. SRH autocorrelation was low in 14 time series (-0.11 to 0.26) with little difference (χ(2) = 6.46, P = 0.91) among them. Fluctuations between better and worse health were very common and poor health was associated with hospital use. It is not clear why some patients continued on a downward trajectory, whereas others who destabilized appeared to completely recover, and even improved over time. CONCLUSION: SRH reflects an individual's complex health trajectory, but as a single measure does not predict when and how deteriorations will occur in this study. Individual patients appear to behave as complex adaptive systems. The dynamics of SRH and its influences in destabilizations warrant further research.

What weekday? How acute? An analysis of reported planned and unplanned GP visits by older multi-morbid patients in the Patient Journey Record System database.

RATIONALE, AIMS AND OBJECTIVES: Timely access to general practitioner (GP) care is a recognized strategy to address avoidable hospitalization. Little is known about patients seeking planned (decided ahead) and unplanned (decided on day) GP visits. The Patient Journey Record System (PaJR) provides a biopsychosocial real-time monitoring and support service to chronically ill and older people over 65 who may be at risk of an avoidable hospital admission. This study aims to describe reported profiles associated with planned and unplanned GP visits during the week in the PaJR database of regular outbound phone calls made by Care Guides to multi-morbid older patients. METHODS: One hundred fifty consecutive patients with one or more chronic condition (including chronic obstructive pulmonary disease, heart/vascular disease, heart failure and/or diabetes), one or more hospital admission in previous year, and consecutively recruited from hospital discharge, out-of-hour care and GP practices comprised the study sample. Using a semistructured script, Care Guides telephoned the patients approximately every 3 week days, and entered call data into the PaJR database in 2011. The PaJR project identified and prompted unplanned visits according to its algorithms. Logistic regression modelling and descriptive statistics identified significant predictors of planned and unplanned visits and patterns of GP visits on weekdays reported in calls. RESULTS: In 5096 telephone calls, unplanned versus planned GP visits were predicted by change in health state, significant symptom concerns, poor self-rated health, bodily pain and concerns about caregiver or intimates. Calls not reporting visits had significantly fewer of these features. Planned visits were associated with general and medication concerns, reduced social participation and feeling down. Planned visits were highest on Monday and trended downwards to Fridays. Unplanned visits were reported at the same rate each weekday and more frequently when the interval between calls was ≥3 days. The PaJR project Care Guides advised patients to make unplanned visits in 6.3% of calls and advised planned GP visits in 2.5% of calls. CONCLUSION: Unplanned GP visits consistently indicated a significant change to worse health with planned visits presenting less acuity in this study of older multi-morbid patients in general practice, when monitored by regular calls at about every 3 days. The PaJR study actively prompted GP visits according to its algorithms. Assessing and predicting acuity in older multi-morbid patients appears to be a promising strategy to improve access to primary care, and thus to reducing avoidable hospital utilization. Further research is needed to investigate the topic on a wider scale.