Ambient Independence Measures for Guiding Care Transitions

The proposed study has the potential to transform current research and clinical practice
paradigms of prediction and decision making about independent living. This is accomplished by
shifting from reliance on episodic, self-reported or crisis event provoked data to the use of
ecologically valid multidimensional and continuous physiological, activity, and behavioral
data. This approach has great potential to substantially improve care need and transition
decisions. In achieving this goal several innovations beyond available systems and ongoing
research are notable. First, grounded by prior studies associating static clinical measures
to future placement outcomes, we now contemporaneously and continuously will acquire
fundamental physiological measures (weight and walking speed), activity and behavioral
measures, thereby improving our ability to proactively discriminate important health and
functional change in real time. Using existing in-home activity data collected longitudinally
in an aging population combined with simulated data from additional new sensed measures
(phone use, medication taking, body composition) we will generate derived novel metrics -
AIMs - to provide objective dynamic measures of activity and behaviors that are essential to
maintaining independence. These metrics will be used to develop prediction algorithms based
on documented transition outcomes from the original data set to be used by care teams (Aim
1). Working care transition professionals will be iteratively queried for the refinement of
these objective measures (Aim 2). These care providers' expertise and understanding of key
changes that impact independence is invaluable to identification of ambient independence
measures that matter, and lead to meaningful care implementation pathways. The efficacy of
the final set of measures chosen and built into a user friendly interface for the care team
to use (Aim 2) will then be tested (Aim 3) by comparing independently living seniors in one
of three comparison groups: 1) installed technology, from which AIMs data will be extracted
and provided to the care transition team to aid in transition decisions; 2) installed
technology, from which AIMs data will be extracted but will not be available to the
transition team; and 3) no technology. We may have insufficient power to recognize
significant change between the validation group and the control group. However, this
primarily study is intended to test the feasibility of the approach, and to identify those
types of AIMs data that are most useful for making transition decisions, which will be used
to inform larger, more definitive studies in the future.

Inclusion Criteria:

- Live alone

- Live independently

- Computer user with internet

Exclusion Criteria:

- Dementia (CDR scale score > 0.5)

- Medical illness that would limit physical participation (e.g. wheelchair use) or
likely to lead to death within three years (e.g. terminal cancer)