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helping howard: unconferences | service design & branding| Spring 2020

helping howard

nutrition optimization and meal coordination service

for elderly adults

Normal activities of daily living become increasingly difficult as people age, especially in tandem with managing chronic conditions. The goal for this project was to develop skills for Amazon Alexa to assist elderly adults aging-in-place. 

my role

user research

UX/UI Design

project manager

the team

Sienna Sun

Shreya Shenai

our approach

tackling ...

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Our question started with, "How might we use voice-assistant technologies as a tool to help elderly adults age-in-place?


Our question became, "How might we alleviate the cognitive load of balancing nutrition needs while managing chronic health conditions?"

what were my responsibilities?

- conducted initial research

- interviewed stakeholders and observed a real unconference

- synthesized findings

- visuals to complement the brand mark

- ran meetings and made sure team stayed on track to accomplish goals

what was the design process?





System Design, Iteration

what was the result?

Helping Howard is a customized meal optimization and provision service, based on the primary user's health conditions. The system coordinates meal delivery using established meal providers in the primary user's immediate social network.


The system has the following capabilities: 

  • Recommends menu options based on user preference & health condition

  • Coordinates meal delivery for primary user, using approved meal providers that the primary caregiver or primary user establish upon set-up

  • Tracks food purchases and consumption, creating reports if needed

  • Takes a load off of caregivers in scheduling and planning meals, but also enables elderly to stay in their homes longer

what did I learn?

At the time of this project, the implementation of a mixed reality conference felt very far out. I never imagined how relevant this would be a few months later due to the covid-19 pandemic.


In retrospect, I wish we did prototyped and evaluated interfaces of the mock platform. We focused our attention on translating insights from research into a proof-of-concept, really researching how emerging technology could be utilized in this type of experience because there were so few examples to go from. 

what was the problem?

research methods

using the ICF model as a need-finding tool

literature review

interviews & ICF mapping


​The ICF (International Classification of Functioning, Disability and Health) is a framework describing and organizing information on functioning and disability.

Research began with literature reviews, existing technology applications, and using the ICF Model to build case examples of realistic aging scenarios. 


It is far more common for the elderly to experience multiple conditions in conjunction with the normal aging process. Not surprisingly, it can be very complicated to manage multiple conditions and the degree of severity impacts the individuals ability to carry on normal activities of daily living, necessary for independent living. Howard, shown , is just one of many cases we developed.

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key insights


What used to be simple things (getting off the toilet, remembering to take a medication, opening a can, going up a step, etc.) become increasingly difficult as individuals age. Inability to do these tasks often results in moving individuals out of their home to age elsewhere.


Some widowed spouses are finding themselves in a situation where they may have to cook for the first time, while others are physically burdened by the effort involved in having a nutritious meal. This is compounded by managing chronic conditions such as diabetes and mild chronic impairment, both of which are common in aging adults.

Not only is this difficult for the aging, but it's worrisome for the family member or caregiver overseeing their care.


Meal preparation and creating nutritionally-rich meals can be extremely challenging for aging adults. With a focus on nutrition, how can we use digital tools to better enable individuals to age-in-place?


Create a simple, intuitive system that

provides actionable, nutrition insights based on user behavior

to ease the cognitive load of meal planning for those with chronic health conditions 

who are the users?

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what are the key components to the design?

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how does the system work?


Helping Howard is both a remote nutritionist (optimizing meals according to primary user's health conditions) and a meal planning and provision service. Friends and family, approved by the user or caregiver, deliver meals to "Howard" (our user in this case).  Amazon Alexa Show is a secondary interface.



The working premise was that the system would learn, drawing from a series of user inputs and pull from different databases to optimize dietary menus for the user. Below is our initial conceptualization of the system and its components. 

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initial wireframes

Early wireframes focused on integrating key functions and determining how different systems would work together. We also explored how this system could work if it used Amazon Alexa as the primary mode of interaction, accompanied by a secondary interface on the Amazon Alexa Show, for example.

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Sienna made this awesome storyboard to give a fuller understanding of how the system could work. 

Concept Refinement

picking the design apart and diving into the details

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blueprint mapping

how will each user interact with the system?

We then dove into the true details of user experience and system integration.

I led ideation exercises, sticky note mapping, and healthy debate so that our team was thorough and meticulous in thinking through system aspects from start to finish. After going through this process quite extensively, we digitized and refined elements of how Helping Howard would function. 


system refinement


sketches of system integration

Journey maps helped us think through key touchpoints in the experience and helped us identify where we needed to refine our system. 

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how do the service components interact with each other?


This blueprint maps out interactions between the primary user (who is receiving meals), the meal provider, and the Helping Howard Ai system. This chart identifies feedback mechanisms, as well as what actions and resources are needed to keep the system running.

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how does the meal coordination system work?


This schematic demonstrates the overarching inputs and outputs that make the system operate. As you can see, success is dependent on involvement by both the primary user and the meal provider. Ceres+ is continually learning through these interactions and is the “manager” running the process on the backend.

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putting it all together


With a clear idea of how the system functions and understanding different user needs, we developed the information architecture for the primary user's interface and for the caregiver to manage and oversee the meals. We made these prior to going medium and high-fidelity prototypes. 

Final Prototypes

mobile interface for howard (primary user)

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alexa show interface for howard (primary user)

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a few last thoughts on the project

(in retrospect)


We probably spent too much time refining the intricacies of how Helping Howard would function. I would have liked to test and evaluate prototypes throughout the process. 


Halfway into this project, my grandmother passed away. It was amazing because this project quickly became intensely personal. I soon personally experienced how useful this could be as we began coordinating care and meals for my 93-year-old grandpa. As adults age with technology, there is a bigger space for services like these. He's now become an expert at ordering all of his groceries online for caregivers to help prepare his meals. 


Finally, in a perfect world, I would have liked to build the voice assistant, the Alexa Show interface, the primary user interface, and the caregiver interface all in one swoop. It was more than a small team could accomplish in a short time, but I'm optimistic for services like this in the future to help us use technology to better care for our loved ones. 

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