Overcoming Barriers to Early Detection with Pervasive Computing

Embedded assessment leverages the capabilities of pervasive computing to advance early detection
of health conditions. In this approach, technologies embedded in the home setting are used to
establish personalized baselines against which later indices of health status can be compared.
Our ethnographic and concept feedback studies suggest that adoption of such health technologies
among end users will be increased if monitoring is woven into preventive and compensatory health
applications, such that the integrated system provides value beyond assessment. We review health
technology advances in the three areas of monitoring, compensation, and prevention. We then define
embedded assessment in terms of these three components. The validation of pervasive computing
systems for early detection involves unique challenges due to conflicts between the exploratory
nature of these systems and the validation criteria of medical research audiences. We discuss an
approach for demonstrating value that incorporates ethnographic observation and new ubiquitous
computing tools for behavioral observation in naturalistic settings such as the home.

Leveraging synergies in these three areas holds promise for advancing detection of disease states.
We believe this highly integrated approach will greatly increase adoption of home health
technologies among end users and ease the transition of embedded health assessment prototypes from
computing laboratories into medical research and practice. We derive our observations from a series
of exploratory and qualitative studies on ubiquitous computing for health and wellbeing.
These studies, highlighted barriers to early detection in the clinical setting, concerns about home
assessment technologies among end users, and values of target user groups related to prevention and
detection. Observations from the studies are used to identify challenges that must be overcome by
pervasive computing developers if ubiquitous computing systems are to gain wide acceptance for early
detection of health conditions.

The motivation driving research on pervasive home monitoring is that clinical diagnostic practices
frequently fail to detect health problems in their early stages. Often, clinical testing is first
conducted after the onset of a health problem when there is no data about an individual’s previous
level of functioning. Subsequent clinical assessments are conducted periodically, often with no data
other than self-report about functioning in between clinical visits. Self-report data on mundane or
repetitive health-related behaviors has been repeatedly demonstrated as unreliable. Clinical
diagnostics are also limited in ecological validity, not accounting for functioning in the home and
other daily environments. Another barrier to early detection is that agebased norms used to detect
impairment may fail to capture significant decline among people whose premorbid functioning was far
above average. Cultural differences have also been repeatedly shown to influence performance on
standardized tests. Although early detection can cut costs in the long term, most practitioners are
more accustomed to dealing with severe, late stage health issues than subclinical patterns that may
or may not be markers for more serious problems. In our participatory design interviews, clinicians
voiced concerns about false positives causing unwarranted patient concerns and additional demands
on their time. Compounding the clinical barriers to early detection listed above are psychological
and behavioral patterns among individuals contending with the possibility of illness. Our interviews
highlighted denial, perceptual biases regarding variability of health states, over-confidence in
recall and insight, preference for preventive and compensatory directives over pure assessment
results, and a disinclination towards time consuming self-monitoring as barriers to early detection.
Our ethnographic studies of households coping with cognitive decline revealed a tension between a
desire for forecasting of what illness might lie ahead and a counter current of denial. Almost all
caregivers and patients wished that they had received an earlier diagnosis to guide treatment and
lifestyle choices, but they also acknowledged that they had overlooked blatant warning signs until
the occurrence of a catastrophic incident (e.g. a car accident). This lag between awareness and
actual decline caused them to miss out on the critical window for initiation of treatments and
planning that could have had a major impact on independence and quality of life. Ethnography and
concept feedback participants attributed this denial in part to a fear of being diagnosed with a
disease for which there is no cure. They also worried about the effect of this data on insurers and
other outside parties. Participants in the three cohorts included in our studies (boomers, healthy
older adults, and older adults coping with illness themselves or in a spouse) were much more
interested in, and less conflicted about, preventive and compensatory directives than pure assessment.

Perceptual biases also appear to impede traditional assessment and selfmonitoring. Ethnography
participants reported consistently overestimating functioning before a catastrophic event and appeared,
during the interview, to consistently underestimate functioning following detection of cognitive
impairment Additionally, we observed probable over-confidence among healthy adults in their ability to
recall behaviors and analyze their relationship to both environmental factors and wellbeing. This
confidence in recall and insight seemed exaggerated given findings that recall of frequent events is
generally poor. As a result of these health perceptions, many of those interviewed felt that the time
and discipline required for journaling (e.g. of eating, sleeping, mood, etc.) outweighed the benefits.
Additionally, they expressed wariness of confronting or being reprimanded about what is already obvious
to them. They would prefer to lead investigations and develop strategies for improving their lives.
Pervasive computing systems may enable this type of integrated, contextualized inquiry if they can also
overcome the clinical and individual barriers that might otherwise impede adoption of the new technologies.

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