# From Personalized Medicine to Population Health: A Survey of mHealth Sensing
Techniques

Zhiyuan Wang, Haoyi Xiong  _Senior Member, IEEE_ , Jie Zhang, Sijia Yang,
Mehdi Boukhechba,
Laura E. Barnes, Daqing Zhang,  _Fellow, IEEE_ , and Dejing Dou  _Senior
Member, IEEE_ Z. Wang, H. Xiong and D. Dou are with the Big Data Lab, Baidu
Research, Baidu Inc., Haidian, Beijing, China.Z. Wang, M. Boukhechba, and L.E
Barnes are with the Department of Engineering Systems and Environment,
University of Virginia, Charlottesville, Virginia, United States.J. Zhang and
D. Zhang are with the Department of Computer Science, Peking University,
Haidian, Beijing, China.S. Yang is with the School of Cyberspace Security,
Beijing University of Posts and Telecommunications, Haidian, Beijing,
China.Corresponding Author: Haoyi Xiong<EMAIL_ADDRESS>
###### Abstract

Mobile Sensing Apps have been widely used as a practical approach to collect
behavioral and health-related information from individuals and provide timely
intervention to promote health and well-beings, such as mental health and
chronic cares. As the objectives of mobile sensing could be either _(a)
personalized medicine for individuals_ or _(b) public health for populations_
, in this work we review the design of these mobile sensing apps, and propose
to categorize the design of these apps/systems in two paradigms – _(i)
Personal Sensing_ and _(ii) Crowd Sensing_ paradigms. While both sensing
paradigms might incorporate with common ubiquitous sensing technologies, such
as wearable sensors, mobility monitoring, mobile data offloading, and/or
cloud-based data analytics to collect and process sensing data from
individuals, we present two novel taxonomy systems from _Sensing Objectives_
and _Sensing Paradigms_ perspectives that can specify and classify
apps/systems from aspects of the life-cycle of mHealth Sensing: _(1) Sensing
Task Creation & Participation_, _(2) Health Surveillance & Data Collection_,
and _(3) Data Analysis & Knowledge Discovery_. With respect to different goals
of the two paradigms, this work systematically reviews this field, and
summarizes the design of typical apps/systems in the view of the
configurations and interactions between these two components. In addition to
summarization, the proposed taxonomy system also helps figure out the
potential directions of mobile sensing for health from both personalized
medicines and population health perspectives.

###### Index Terms:

Mobile Health (mHealth), Mobile Sensing, Mobile Crowd Sensing (MCS), and
Personal Sensing.

##  I Introduction

Mobile Sensing [1] refers to a sensing paradigm leveraging ubiquitous sensors
embedded in mobile devices (e.g., mobile phones, smartwatches) to monitor the
environments, human behaviors, and interactions between human and environments
in a human-centric manner[2, 3]. Lots of work studied the adoption of mobile
sensing techniques in health domains [4, 5, 6] such as mental health [7] and
chronic cares [8]. Early visionary works [9, 10] proposed the basic framework
of mobile health (mHealth) sensing techniques in nowadays that leverage “non-
invasive” mobile sensing schemes [11] to collect data for human activities
recognition and infer the individual’s health status using machine learning
algorithms with longitude and real-time sensory data accordingly [7, 12, 13,
14].

Compared to traditional medical sensors that are frequently operated by health
professionals to collect data from patients in clinic contexts, mHealth
sensing relies on participation of voluntary users to obtain information for
health-related well-beings in their daily life [15, 11, 16, 17, 18].
Furthermore, the goal of mHealth sensing research is to study the innovative
applications of mobile sensing techniques to collect behavioral data related
to health and well-beings, while medical sensing aims at designing new
measurement and instrument techniques for medical purposes [19]. More
comparison between mHealth sensing and medical sensing could be found in
Appendix.A. In this work, given the rapid development in such area, we propose
to review and survey the recent progress in mHealth sensing techniques.

###  I-A Health Issues and Health Outcomes of mHealth Sensing Apps/Systems

There are several works reviewing and surveying the research problems [20, 21,
22, 23, 24, 25], emerging techniques [26, 27], system design [28, 29, 30], and
prototyping tools [31, 32, 14] for mHealth Sensing apps/systems. In this work,
we propose to first categorize the research works on mHealth sensing
apps/systems with respect to the major health issues (e.g., depression and
anxiety) that are covered by mHealth.

Furthermore, for every major health issue reviewed here, we also discuss
mHealth sensing research from the perspectives of _personalized medicine_ and
_population health_ — two major health outcomes of modern healthcare[33, 34,
35]. Specifically, we would like to survey and compare the mHealth sensing
techniques that handle to the health issues for either personalized medicine
or population health purposes. In our work, we define these two outcomes as
follows.

  * •

Personalized Medicine. The personalized medicine focuses on individual
patients– _“with medical decisions, practices, interventions and/or products
being tailored to the individual patient based on their predicted response or
risk of disease”_ [36]. Thus, the objective of personalized medicine is to
improve and optimize the individual treatment effects through sensing,
monitoring, and predicting their health status [37, 38].

  * •

Population Health. The population health is defined as _“the health
outcomes[39] of a group of individuals, including the distribution of such
outcomes within the group”_ [40]. The goal of population health is to promote
the health of an entire human population [41], where the approaches include
discovering health outcomes, understanding patterns of health determinants,
and policy making for interventions.

Thus, the first part of this survey includes a comprehensive review on the
research related to mHealth sensing apps/systems targeting at various health
issues from the perspectives of population health and personalized medicine.
In Appendix.B, we introduce the procedure that we collect and select the
publications and health issues for review.

Figure 1: The three-stage pipeline of mHealth Sensing apps

###  I-B Objectives and Design & Implementation (D&I) Issues of mHealth
Sensing Apps/Systems

For the two health outcomes of mHealth sensing, we plan to generalize and
categorize existing works into two design paradigms – _(i) Personal Sensing
(PS)_ and _(ii) Crowd Sensing (CS)_ paradigms, according to the significant
differences in app designs, such as user engagement strategies [42, 43] and
data analysis approaches [44, 45]. Moreover, as shown in Figure 1, we follow
the common frameworks of mobile sensing apps [1, 46] and propose to modularize
the design of mHealth sensing apps [47] for both Personal Sensing and Crowd
Sensing into a pipeline of three stages as follows.

  1. 1.

_Sensing Task Creation & Participation - _ With a pool of potential mobile
users, the mHealth Sensing organizers create tasks for specific health issues
via deployed apps [4, 48, 49], then prompt the participation of the users [10,
50, 51] or recruitment participants with incentives [52, 53].

  2. 2.

_Health Surveillance & Data Collection -_ With actively engaged participants,
the mHealth Sensing apps and systems collect health-related data from
participants in their daily life scenarios [11, 54], then store and offload
the sensing data with security and privacy-protection guarantees [55, 56, 57,
58].

  3. 3.

_Data Analysis & Knowledge Discovery -_ With health-related data collected,
the mHealth Sensing apps and systems carry out data processing and analysis
under ethical certification[59, 60, 61] to predict health-related events for
individuals[62, 63] and discover determinants of health [40] – i.e., knowledge
about population health and well-beings[10, 64].

Based on above two mHealth sensing design paradigms and the pipeline of three
stages, this work provides two taxonomy systems that cover the major technical
challenges and methodologies in this area. Specifically, we focus on the
“objectives” (e.g., data privacy, data quality, energy efficiency, and other
goals desired to enable practical mHealth sensing apps/systems) and “designs &
implementations (D&I)” (e.g., methodologies to achieve the objectives)
respectively. Furthermore, we review and categorize the sensing objectives and
D&I issues by the combination of two mHealth sensing design paradigms and
three stages in details. Note that we discuss our motivations to structure
these two taxonomy systems in Appendix.C.

###  I-C Organization of the Survey

The rest of this manuscript is organized as follows. Section II reviews
several typical mHealth Sensing apps/systems for seven common health issues
with case studies in details, where we specifically discuss ways mHealth
sensing techniques handle the health issues for population health and
personalized medicine purposes. Section III introduces the taxonomy system
classifying mHealth sensing apps/systems from the _sensing objectives_
perspective. Section IV presents the taxonomy system the classifies the
mHealth sensing apps/systems from the perspective of _sensing paradigms and D
&I issues_. In Section V, we point out identified research gaps and future
directions in mHealth Sensing, while Section VI concludes the article. In
Appendix, we review the scientific approaches to this survey.

TABLE I: Terms and Definitions Terms |  Definitions
---|---
Mobile Health |  Namely _mHealth_ , a term used for the practice of medicine and public health supported by mobile devices [23]
mHealth Provider |  Peoples (e.g., researchers, business companies) who organize healthcare services by designing and deploying mHealth techniques and applications [65].
Ubiquitous Sensing |  Using networked sensors pervasively exist in daily-life scenarios to capture information about humans, environments, and their interactions [66]
Personal Sensing |  A technique collecting and analyzing data from sensors embedded in the context of individual’s life with the aim of identifying his/her behaviors, thoughts, feelings, and traits [3]
Crowd Sensing |  A technique where a large group of individuals having mobile devices capable of sensing and computing collectively share data and extract information to measure, map, analyze, estimate or infer (predict) any processes of common interest [67]
Passive Sensing |  Sensing via devices that detect and respond to some type of input from the physical environment [68]
Health Status |  One’s medical conditions (both physical and mental), claims experience, receipt of health care, medical history, genetic information, evidence of insurability, and disability [69]
Health Outcomes |  Health events occurring as a result of interventions [70]
Health Determinants |  Conditions which contribute to a wide range of health and quality of life-risks and outcomes [71]
Mental Health |  A state of well-being in which the individual realizes his/her own abilities, can cope with the normal stresses of life, can work productively and fruitfully, and is able to make a contribution to his/her community [72]
Treatment Effects |  Causal effect of a given treatment or intervention on an outcome variable of health interests [73]
Health Benefit |  Positive phenomenons that a medicine treatment, substance or activity is improving health [74]
Health Intervention |  A treatment, procedure, or other action taken to prevent or treat disease, or improve health in other ways [75]
Digital Biomarkers |  Objective, quantifiable physiological and behavioral data that are collected and measured by means of digital devices [76]

##  II Reviewing mHealth Sensing Apps and Systems for common health issues

In this section, we first list the definitions of some health-related terms in
Table I. Then, with respect to seven most commonly researched health issues in
reviewed papers, we review and summarize typical objectives and applications
of mHealth Sensing works surrounding the seven issues (i.e., depression and
anxiety, sleep quality and insomnia, diabetes, heart, elder-care, diet
management, tinnitus, and COVID-19) from the two mHealth Sensing perspectives
(i.e., _(a) Personalized Medicine_ and _(b) Population Health_).

TABLE II: A summary table of the mHealth apps on seven common health issues created for the motivations in (a) Personalized Medicine and (b) Population Health respectively Health Issues | Personalized Medicine | Population Health
---|---|---
Motivations | Examples | Motivations | Examples
| Depression
---
and Anxiety
| Self-identifying and reducing
---
depression and anxiety
|  [77], [78],
---
[79], [13],
[80], [50]
| Population mental health screening
---
and determinants inferring
|  [81], [82],
---
[54], [83],
[84]
| Sleep Quality
---
and Insomnia
| Monitoring and interventions
---
to promote sleep quality
|  [85], [48],
---
[86],[12]
| Population sleep statistics for
---
understanding sleep science issues
|  [87], [88]
---
Diabetes (type 2) |  | Glucose monitoring for type
---
2 diabetes management
|  [8], [89],
---
[90], [91]
| Understanding the social determinants
---
contributing to diabetes
|  [92], [17]
---
Heart |  | Heart rate monitoring and
---
heart disease prevention
|  [93], [94],
---
[95], [96],
[97], [16]
| Researching the impact of determinants
---
on cardiovascular diseases
[98]
Elder-care |  | In-home care service and
---
assistance
|  [99], [100]
---
Understanding the health status and lifestyle of the elderly population | [101]
| Outdoor monitoring and
---
notification
|  [102], [103]
---
| Diet and Weight
---
Management
| Diet self-monitoring and
---
exercise management
|  [62, 63],
---
[104, 17]
| Understanding population eating
---
patterns, episodes, and disorders
[105]
Tinnitus |  | Tinnitus self measurement
---
and retraining therapy
|  [106], [107]
---
| Studying symptoms, causes, and
---
treatments of tinnitus population
|  [108], [109],
---
[110], [111]
COVID-19 |  | Automatic self-diagnosis
---
|  [112],[113],
---
[114], [115]
| Population screening the spread of
---
COVID-19
|  [116, 117],
---
[118, 119]
| Contact tracing for infectious
---
risk estimation
|  [120], [121],
---
[122], [123]
| Public Health Policy Evaluation and
---
Development
|  [124, 125, 126],
---
[127, 128, 129],
[130, 131, 132]

In general, as shown in Table II, for _Personalized Medicine_ objective, apps
adopt _Personal Sensing_ paradigm and focus on individual’s health benefit via
D&Is of health status monitoring, recognition, and intervention; while for
_Population Health_ , the apps in a _Crowd Sensing_ manner mainly aim to
measure/understand population health status and discover knowledge for public
health benefit.

Here we discuss three typical health issues among the seven from both
perspectives of mHealth Sensing.

###  II-A Depression and Anxiety

Depression and anxiety are the main mental health disorders, broadly
experienced by 548 million people worldwide who hardly access effective
treatment [133, 134]. mHealth Sensing gives ubiquitous and flexible solutions
on both sides of personal mental health monitoring and intervention [135, 136,
137, 138, 49, 139] and population mental health surveying and understanding
[140, 141].

#### Personal Depression and Anxiety Monitoring and Intervention

mHealth Sensing techniques are providing broadly accessible services for
individuals with mental disorders, as they can collect user’s daily-life
indicators varying with time and scenarios, as well as deliver timely
interventions though without scarce clinical resources [27]. A typical
application of Personal Sensing for depression and anxiety is Mobilyze! [78],
a mobile mental intervention application with a two-step framework – context
sensing and ecological momentary intervention. By collecting contextual data
such as locations, recent calls, ambient light, and feeding them into a
medical diagnosis model, it infers user’s mental health status and provides
interventions to guide to overcome psychological dilemmas (e.g., lack of
social interaction). Furthermore, in mental health domain, the concept of
just-in-time adaptive intervention (JITAI) was proposed to guide timely and
personalized interventions [18]. For example, advances in artificial
intelligence are promoting smarter decision-making of when and where it is
most helpful to provide supportive interventions by learning from individual’s
historical behaviors [142, 143].

#### Population Depression and Anxiety Survey

Mobile Sensing techniques are increasingly being adopted to population
depression and anxiety surveys, as they provide a low-cost (both in resources
and time), widespread, and online data collection manner versus laborious and
high-cost clinical testing and questionnaires. For example, by studying the
correlation between anxiety and behavioral indicators (e.g., activity
locations, text messages, and calls) in a 54-students group over two weeks,
Boukhechba _et al._ [83] proposed flexible anxiety assessment methods for
monitoring college students via mobile apps.

#### Population Mental Health Determinants Understanding

New inspirations and knowledge about population mental health determinants can
be gained via massively collecting and comparatively analyzing data among
populations [81], such as inferring causes between social anxiety and group
behavioral patterns [144]. For example, a Mobile Crowd Sensing platform –
Sensus [4] was leveraged by Chow _et al._ to verify clinical models of
depression and anxiety [82]. Taking the levels of depression and social
anxiety as moderators, researchers tested the relations between state effect
and time spent at home of 72 recruited students, and finally, they gain an
understanding on the significant correlations between depression & anxiety and
home-stay behaviors in the target population.

###  II-B Sleep Quality and Insomnia

mHealth sensing applications are widely applied to monitor sleep status and
measure sleep quality. The basis for this is that the digital biomarkers
(e.g., heart rate and sound of snoring) related to sleep can be easily
collected by mobile sensors during sleeping [145].

#### Personalized Sleep Monitoring and Insomnia Assistance

mHealth Sleeping apps are giving more and more accessible sleep quality
monitoring and sleep-aid services to users [146, 85, 145, 12]. Several sleep
monitoring systems are deployed on wearable devices requiring users to wear a
product embedded with specific sensors during sleeping, which is either
limited in clinical environment [147] or uncomfortable to the users [148]. A
new trend in mHealth Sensing for sleep monitoring is using off-the-shelf
mobile phones built-in sensors such as microphones and accelerometers to
detect the sleep duration and infer sleep quality. For example, Hao _et al._
[48] proposed to leverage microphone audio to detect the events closely
related to sleep quality such as ambient noise, body movement, and snoring
[85] to enable personalized and in-place sleep quality monitoring.
Furthermore, Gu _et al._ [86] mined and detected the sleep stage (e.g., week
sleep, deep sleep, and rapid eye movement) by monitoring sleep environment and
personal factors leveraging a statistical model, which provides fine-grained
descriptions of sleep status.

#### Population Sleep Science Research

Crowd Sensing apps are widely used to create population sleep status datasets
for sleep science, such as understanding the issues on psychological research
and sleep science, as one’s sleep quality interacts with her/his lifestyle and
mental status. In practice, to understand the behavioral pattern between phone
usage and sleep quality, recently, Sharmila _et al._ [87] collected a large-
scale phone usage dataset and sleep questionnaires from 743 participants of
different ages and socioeconomic backgrounds in a Crowd Sensing manner and
figure out the effect of mobile phone usage patterns on sleep using
statistical methods. Abdullah _et al._ [88] proposed to study the effects of
sleep quality on people’s daily rhythm and well-being including levels of
alertness, productivity, physical activity, and even sensitivity to pain.

###  II-C Mobile Sensing in COVID-19 Era

The mobile devices that people carry around are like "witnesses" to the spread
of the epidemic, as the spread of the COVID-19 virus is accompanied by human
mobility and contact, where Mobile Sensing have shown its great power in
COVID-19 era [149]; the typical contributions include personal diagnosis
[112], infection traceability [120], transmission interpretation [118] and
policy decision-making [150], etc.

#### Personal Automatic Self-Diagnosis via Sounds

Mobile microphones collect audio samples such as sighs, breathing, heart,
digestion, vibration sounds on body, which can serve as the indicators to
diagnose lung diseases [151], giving great possibilities of automatic
detection and diagnosis of COVID-19 infection [113]. For example, Brown _et
al._ [114] proposed methodologies to detect diagnostic signs of COVID-19 from
voice and coughs, which well distinguish a user who is COVID-19 positive with
a cough from a negative user with a cough. In addition to voice analysis, Han
_et al._ [115] further explored fusion strategies to combine voice and
reported symptoms which yield better detection performance.

#### Personal Contact Tracing

The COVID-19 virus spreads from an infected person’s mouth or nose in small
liquid particles when they cough, sneeze, speak, sing or breathe [152],
causing finding the contacts of positive patients is an essential task for
epidemic control. Many contact tracing mobile apps are developed and deployed
for privacy-preserving and comprehensive COVID-19 tracing for individual users
to check their contact history with mobile phone data [120, 121, 123, 153].
For example, Carli _et al._ [122] developed WeTrace, a mobile COVID-19 tracing
app which detects and records one’s contact with others leveraging the
interaction via Bluetooth Low Energy (BTE) communication channel; and a
trusted data transmission framework is proposed to balance the health and the
privacy perspectives.

#### Epidemic Spreading Analysis via Human Mobility

The significant correlation between human mobility and COVID-19 infections
provides guidance on investigating and understanding the spreading of COVID-19
via multi-scale human mobility data [116, 117, 118]. From the perspective of
the human mobility research, large-scale and long-term GPS data can be used to
detect high-risk regions [154], and population traveling data (e.g., Baidu
Qianxi [155]) can be leveraged to analyze the spreading path between cities
and countries [156]. For example, by incorporating human mobility data into
epidemic modeling, Hao _et al._ [119] studied how the multi-scale urban human
mobility impacts the spreading process at varying levels, which provides
insights on making smarter policies to respond the next outbreak.

#### Public Policy Evaluation and Making

Strict infection control policies proposed by governments have been taken to
limit and mitigate the fast-spreading of COVID-19, such as lock-down, travel
restrictions, quarantine, social distance ban. mHealth Sensing data among
populations is contributing in evaluating and making these policies [157,
150]. Intuitively, several of the sensing indicators among populations, such
as the average time of users stay-at-home and the number of mobile devices in
a public place, can be used to measure stay-at-home and social distance policy
efficiency [124, 125]. Furthermore, statistical and machine learning methods
can be used to estimate, simulate, and predict the effects of the policies on
controlling virus spreading driven by the population data gathered in mobile
devices [127, 128, 130].

###  II-D Discussion

Note that, in this work, we review and summarize the works on mHealth Sensing
Apps and Systems that deployed over massive smartphones and commodity
interactive devices, such as tablets, smartwatches, and other wearable
consumer electronics in non-invasive sensing manners. Many other works
intending to monitor physiological status of patients for medical purposes or
professional devices/systems for critical cares/assisted living, such as
medical sensors [158, 159, 160, 161, 162], Internet of Medical Things (Medical
IoTs) and Medical Cyber-Physical Systems (Medical CPSs) [163, 164, 165, 166,
167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181,
182, 183, 184, 185], and medical robots [186, 187, 188, 189, 190, 191], are
not included here. Of-course, there are many other behavior-related health
issues that are not well covered here, such as drug/alcohol abuses or
addiction [192, 193] in general.

Figure 2: Taxonomy System I – Sensing Objectives

##  III Taxonomy System i: Classification of mHealth Sensing by Sensing
Objectives

In this phrase, we introduce the proposed mHealth Sensing taxonomy system I
from _sensing_ _objectives_ perspective. With respect to the two perspectives
of modern healthcare, as shown in Figure 2, we specify and classify _Major_
_Sensing_ _Objectives_ of mHealth Sensing apps as _(a)_ _Personalized_
_Medicine_ and _(b)_ _Population_ _Health_ apps. Then we further discuss the
_Detail_ _Sensing_ _Objectives_ in each step of the life-cycle framework of
mHealth Sensing.

###  III-A Objectives in Sensing Task Creation & Participation

The main objectives in Sensing Task Creation & Participation step are creating
and allocating health-related tasks in mobile apps, then prompt the
participation of the users or recruited participants to execute the sensing
tasks. Since the health benefit for the participants in the two types of apps
varies (i.e., participants in _Personalized Medicine_ apps obtain direct
personal health benefit, while participants in _Population Health_ practices
hardly obtain health benefit equaling to their efforts), where the detail
objectives in this step can be distinguished as _service provision for
personalized medicine seekers_ and _recruitment for population health
participants_.

  * •

Service Provision for Personalized Medicine Seekers - Sensing apps for
_Personalized Medicine_ provide accurate health status monitoring and
personalized interventions or treatments, which can be concluded as healthcare
_services_ provision [194, 18, 195]. In most of the _Personalized Medicine_
cases, participants actively engage in the sensing task for personalized
medicine with an expectation to seek and extend personal health benefit [196].
To this end, the detail objective of personalized medicine apps in this step
is to provide exact healthcare services (e.g., exercise reminders and user-
friendly interface) and keep improving service quality (e.g., optimizing
intervention times with algorithms) to guarantee and enhance users’ active
engagement [197].

  * •

Recruitment Provision for Population Health Participants - _Population Health_
apps are mostly for studying population health issues leveraging massive
collected data from groups, causing a problem for participants is that – there
is no intuitive and sufficient health benefit gained for themselves to
compensate for their costs and concerns (e.g., time consumption, privacy
exposure [198], and battery usage [199]). For example, in a COVID-19
infectious population screening [200] or a rare clinical disease causes
understanding program [201], the results are valuable for organizers but
limited for participants. The above reasons lead to a unique detail objective
of Population Health apps in absorbing participation – providing recruitment
to gather participants and motivating their performance with incentives [202,
53].

###  III-B Objectives in Health Surveillance & Data Collection

With exact sensing tasks and a pool of users/participants, the bottleneck in
_Health Surveillance & Data Collection_ is – how to effectively collect and
gather trustworthy sensing data, with taking users’ costs and concerns into
consideration. As Figure 3, we summarize that mHealth Sensing apps’
trustworthiness lies in _data quality_ and _data quantity_ ; further, the
_data quality_ can be further indicated as _data precision_ and _data
fidelity_ , and the _data quantity_ can be divided into _longitudinal
coverage_ and _population coverage_. In addition, some objectives are commonly
existed in both Personalized Medicine and Population Health practices, such as
_security & privacy_ and _resources consumption_ , but vary in details, where
we reviews these issues in the last of this section.

Figure 3: Detail sensing objectives in Health Surveillance & Data Collection

#### Personal Sensing for Personalized Medicine

In _Personalized Medicine_ mobile apps, to provide timely and adaptive
healthcare services based on precise and sufficient data, the detail
objectives in this step are _data precision_ and _longitudinal coverage_ in
data collection process.

  * •

Data Precision - The data precision is the most straightforward pursuit of
Personal Sensing tasks, which determines the service quality of Personalized
Medicine. Here we give the mobile medical devices in the intensive care unit
(ICU), which is the last barrier to save the lives of dying patients in the
hospital, as examples [203, 204]. The personal wearable devices with incentive
body sensors, light and sound sensors, and others precisely collecting the
physical and environmental context data from ICU are typical Personal Sensing
schemes with high sampling precision, finely sensing physical and
environmental biomarkers such as facial expressions, functional status
entailing extremity movements and postures, and environmental indicators for
ICU’s context [205].

  * •

Longitudinal Coverage - Data with large longitudinal helps understanding
personal health issues comprehensively for two fold reasons – not only
longitudinal moment-to-moment data sampling is helpful for capturing complex
health dynamics to achieve meaningful modelling and prediction [206, 14, 207,
208], but also the analyzing of the onset of some diseases is not trivial, as
the disease might be triggered by the interaction of multiple pathogenic
determinants over a long period (e.g., monthly and annually), which cannot be
detected with a brief observation [209, 210, 211]. In addition, the
interaction of mHealth Sensing apps is also beneficial to the enlargement of
longitudinal coverage of data collection, which shares and gathers information
between apps. For instance, Google Health and HealthVault are cross-platform
personal health record systems storing and sharing information between mHealth
apps in a secure and privacy-protected manner, which gives mHealth apps a
great potential for comprehensively serving health and well-being [212].

#### Crowd Sensing for Population Health

In _Population Health_ practices, the task of Health Surveillance & Data
Collection is to build a large-scale and error-free data pool surrounding the
health issues to be analyzed and researched, with detail objectives of
ensuring _data fidelity_ and enlarging _population coverage_ in the sensing
process.

  * •

Data Fidelity - Versus data precision, data fidelity in the mHealth Sensing
context refers to that there is no human error (e.g., intentional cheating or
equipment failure) in the gathered data [213, 214, 215]. Especially, different
from the collections of some general datasets (e.g., traffic speed data or
urban temperature data) which can be gathered in a short time, collecting
daily/clinical health-related data requires enormous manpower, incentive cost,
and devices resources in a long time [216, 217]. Also, once human errors are
introduced into the data pool, it would causes biased health modeling,
inaccurate treatment effect measurement, and wrong medical conclusions, which
are harmful to the health and well-being purposes [218].

  * •

Population Coverage - Enlarging the population coverage of Health Surveillance
& Data Collection is beneficial to obtain statistical-significant and
generalized Data Analysis & Knowledge Discovery. Specifically, in Crowd
Sensing paradigm, some general guarantees for population coverage are age,
gender, region, patient groups coverage; for varied research purposes, the
coverage requirements for population attributes vary [219, 220]. For example,
data for population mental health researches should cover balanced genders and
diversified ages for comparative analysis and knowledge discovery with
no/limited prior knowledge leveraging machine learning [221] or statistical
inference [222] approaches; data for sleep science researches should cover
kinds of patient groups such as sleep apnea, insomnia, Parkinson’s disease,
and periodic limb movement disorder (PLMD), as well as healthy people as
control group.

In addition, though the detail sensing objectives in Health Surveillance &
Data Collection step are specified as the above perspectives, these objectives
are usually overlapped. For example, _data precision_ and _longitudinal
coverage_ are also meaningful in _Crowd Sensing for Population Health_
practices, but compared to these two objectives, _data fidelity_ and
_population coverage_ are in need of relatively dedicated D&Is for specific
existing problems.

#### Commonly Existed Objectives – Concerns & Costs

Beyond the technical objectives in trustworthiness of data, other issues in
solving users’ practical concerns and costs are the common objectives for
mHealth Sensing apps.

  * •

Security & Privacy - Issues in security and privacy are greatly concerned in
health-related domains, as health data is top sensitive [55, 153]. As for
Personalized Medicine mobile apps, the security/privacy issues include
identity privacy [223] (participants do not want to expose personal
information), data privacy [224] (health-related data is top sensitive),
attribute privacy [225] (for attributes such as locations and trajectories).
Besides, the risk of privacy leakage in Population Health apps is greater
[226, 227], as it requires regular sensitive health-related data uploading and
offloading between mobile devices and cloud servers via networks [228]. To be
specific, additional privacy concerns in Population Health data collecting and
uploading processes are task privacy [229] (the sensing tasks may correlate to
participants’ illnesses), and decentralized privacy [230] (frequent
communication with a central server could be more easily hacked).

  * •

Resources Consumption - Keeping mobile sensing data sampling causes
considerable battery, hardware, and software resources consumption, for every
mHealth Sensing participants. From _Personalized Medicine_ perspective, the
resource consumption is more intense, as its data collection actions are
generally continuous and intensive [231]. Against this background, the type
and combination of sensors in working and their sampling rate, data accuracy
and sampling abundance are under consideration [232, 233]. From _Population
Health_ perspective, when the hardware consumption of each individual’s
perception is already relatively economical, the decrease in resources
consumption are mainly achieved optimizing the task allocation in spatial,
temporal, participants, and content to achieve cost-effective globally sensing
[234, 235, 236, 237, 64].

Worth mentioning, the pursuit of data _trustworthiness_ may increase the
_concerns & costs_ of users; also, _concerns & costs_ also limit the
intensive, longitude, and broad-coverage data sampling of users. It leaves app
developers to make an _optimal tradeoff_ between the two objectives in
practice, as shown in Figure 3. On the one hand, the developers should design
and develop the apps with certifications that minimize data access privileges
subject to the actual needs, to release the _costs & concerns_. On the other
hand, advances in resources saving and privacy protection approaches may make
it possible for developers to obtain additional permissions from users, which
further improves the apps’ _trustworthiness_.

###  III-C Objectives in Data Analysis & Knowledge Discovery

After gathering expected personal or population data pools, the main objective
in Data Analysis & Knowledge Discovery fold is to discover health-related
knowledge about individuals and populations from gathered data, and provide
adaptive and timely healthcare as feedback [238, 239].

#### Personal Sensing for Personalized Medicine

Apps for Personalized Medicine usually recognize [240] or predict [241, 242]
individual user’s health status by integrating his/her historical, as well as
current physical and environmental data surrounding a specific health issue to
accurately recognize/predict health risks and provide precise healthcare
interventions at the right time, as shown in Figure 4.

Figure 4: The comparison of main objectives of Data Analysis & Knowledge
Discovery in the apps for Personalized Medicine and Population Health

  * •

mHealth Accuracy in Risk Prediction - Effective personalized healthcare
services rely on the accuracy in the health status modeling and progression
prediction. Sufficient multimodal data collected user’s daily life such as
self-reported medical history, physical biomarkers (e.g., heart rate), and
environmental biomarkers (e.g., locations) provides great information for
accurately modeling and predicting one’s health outcomes and progressions via
machine learning approaches [243, 244, 245]. For example, by passively
monitoring schizophrenia patients’ psychiatric symptoms represented by 7-item
scale scores and behavioral/contextual characteristics (e.g., physical
activity, conversation, mobility) over months, Wang _et al._ [246] proposed a
prediction system which predicts psychiatric symptoms’ dynamics and
progression merely based on mHealth Sensing data without traditional self-
reported ecological momentary assessment (EMA).

  * •

mHealth Precision in Predictive Intervention - A typical detail objective in
this step for Personalized Medicine apps is to provide predictive
interventions with high mHealth precision responding to recognized/predicted
health outcomes and progressions (e.g., increasing depression and anxiety,
exposing to high heart risk, and being damaged hearing). Specifically, the
precision above lies on precise intervention timing, measures, and intensity,
which leads to just-in-time, adaptive, and effective mHealth supporting
services [18, 247]. For example, Costa _et al._ [136] proposed to improve
one’s cognitive performance by unobtrusively regulating emotions with
smartwatch notifications in varying detected heart rates. Lei _et al._ [248],
by formulating the intervention tasks in real-time as a contextual bandit
problem, provided an online actor-critic algorithm as an intervention strategy
to guide JITAI practices.

#### Crowd Sensing for Population Health

Crowd Sensing practices investigate population health issues by
comprehensively mining massive health-related data among researched groups
such as monitoring and screening the population health status in a region in
both depth and coverage [245, 249], and verifying [87] and inferring [84] the
determinants of specific diseases via powerful statistics-based approaches.

  * •

Depth and Coverage of Population Health Monitoring in Communities - For
Population Health apps (especially for the apps on population health
monitoring, screening, and surveying), in terms of data analysis, it is
meaningful to deeply mine and widely enlarge the information of targeting
communities leveraging collected Crowd Sensing data. For example, in many
mHealth Crowd Sensing practices, some specific characteristics of health
problems (e.g., the contact infection of infectious diseases [250], familial
heredity phenomenon of genetic diseases [251], and regional relevance of
conventional health habits [252]) give great possibility to finish a mobile
population health screening of the whole community by only investigating a
subset of this group, which is a manner with accuracy guarantee and lower
cost.

  * •

Statistical Power of mHealth Approaches in Knowledge Discovery - The
statistical power of the mHealth approaches is a key pursuit for medical-
related knowledge discovery in large-scale population data. Specifically, in
mHealth field, Crowd Sensing is being used as a useful tool to collect and
analyze massive population health-related data to obtain medical knowledge,
where new knowledge can be summarized or inferred by statistical methods for a
better understanding of health determinants [71], such as staying home too
long causes mental health problems [83], lacking exercise would increase the
risk of heart attack [95], and listening too loud music leads to tinnitus
[107]. For example, Zhang _et al._ [220] revealed how human mobility features
extracted from large-scale human mobility data affect one’s health conditions
and which group of features contribute significantly leveraging statistical
approach – shapely additive explanation value analysis, which shed light on
how to understand human mobility data in health monitoring domain.

#### Commonly Existed Objectives – Risks and Ethical Issues

In both mHealth-based _Personalized Medicine_ and _Population Health_
knowledge discovery practices, some risks and ethical issues cannot be ignored
in sensing objectives.

Figure 5: The relationship between the objectives of Personal Sensing and
Crowd Sensing paradigms

  * •

Risks and Ethical Issues - Risks and ethical issues are crucial in human-
subject and mHealth research, since personally identifiable health-related
data of users would be collected, uploaded, and analyzed, as well as sensitive
scientific study results would be made public to varying degrees, even if some
certifications are issued by the developers [253, 254]. For instance, funded
by three-party advertisers, such as insurance companies, developer may
exposure information to them; some patients and victims may be forced to pay
more or even fail to apply, which goes against ethics [255]. Besides of
revealing private health information, common risks and ethical issues in
mHealth Sensing apps include data loss, theft and hacked [256], excessive or
unauthorized collection of data [257], loose medical conclusions and negative
impact on life [258, 259]. Besides, the scientific studies carried with
mHealth apps may be not solid enough, since most of the obtained conclusions
are based on limited observation samples and periods; for example, few studies
have conducted follow-up studies on large-scale populations for more than a
few months, and exact long-term impact of mHealth sensing apps on personal and
population health is still not scientifically clarified [260]. Against this
background, appropriate analysis of potential risks [259], ethical issues[261,
262], as well as previously mentioned security & privacy issues should be done
ahead of issuing certifications of mHealth apps being used in daily-life and
even medical scenarios.

It is worth mentioning that, applying Crowd Sensing apps can be regarded as a
accumulation of the number of Personal Sensing apps deployed in a community.
Thus, most of the objectives in Personal Sensing are also what the Crowd
Sensing paradigm pursues in practice. To this end, here we conclude the
objectives of Personal Sensing, Crowd Sensing, and mHealth Sensing as shown in
Figure 5, where their objectives are progressive. For example, intuitively, in
Crowd Sensing practices, improvements in cost saving and data accuracy also
certainly prompt the performance of the apps.

Figure 6: Taxonomy System II – Sensing Paradigms and D&Is

##  IV Taxonomy System ii: Classification of mHealth Sensing by Sensing
Paradigms and D&Is

With respect to distinguished sensing objectives (i.e., _Personalized
Medicine_ and _Population Health_) and their details discussed in Section III,
two sensing paradigms (i.e., _Personal Sensing_ and _Crowd Sensing_) are
correspondingly proposed to deal with related technical issues through
detailed D&Is. In this phrase, as shown in Figure 6, for each step of mHealth
Sensing life cycle, varying detailed D&I issues of the two sensing paradigms
are discussed.

###  IV-A Design & Implementation Issues in Sensing Task Creation &
Participation

To prompt the users’ participation and task execution leveraging _services_
and _recruitment_ respectively for _Personal Sensing for Personalized
Medicine_ and _Crowd Sensing for Population Health_ apps discussed in III-A,
in this section, we intend to specify the detail D&I issues of the two
paradigms as followings.

#### Personal Sensing for Personalized Medicine

The promotion of user engagement in Personalized Medicine apps is by providing
_services_. Here we discuss two typical forms of user engagement services –
_clinical health service and human-computer interaction (HCI)_ and
_gamification and attraction_ in detail.

  * •

Clinical Health Service and HCI - Providing straight-forward and effective
clinical health service with good HCI design for user experience is the most
intuitive way to increase users’ active engagement, since the essential
motivation of the users downloading the app is to obtain personal health
benefit [263, 264]. In practice, user engagement strategies can be organized
as setting sensing health-related targets around users’ personalized
objectives, delivering adaptive therapeutic feedback including positive
reinforcement, reflection reminders, and challenging negative thoughts [265,
266], and designing easy-to-use platforms [199]. For instance, Cai _et al._
[267, 268] propose to prompt an adaptive and passive personal mobile sensing
framework to provide ecological momentary assessment and intervention services
based on the reinforcement learning techniques, which significantly increased
user engagement in healthcare apps.

  * •

Gamification and Attraction - Gamifying the mHealth Sensing apps for providing
entertainment would promote user engagement, as not only the mobile sensing
data can be used as input for gamification [269], but also mobile apps are
excellent and prevailing mobile carriers for pervasive entertainment [270]. In
practice, gamification strategies are widely applied in _Personalized
Medicine_ apps to promote participation such as self-report data collection
[271, 272] (e.g., setting the goals of the game as the indices to be sensed),
data pre-analysis on client [273] (e.g., pop-up windows asking the user about
the activity and status when the app detects a sequence of abnormal indices),
and health intervention wrapping [274] (e.g., relaxing users under depression
via games). Typically, Rabbi _et al._ [272] designed an app named SARA, which
integrates gamified engagement strategies including contingent rewards, badges
for completing active health tasks, funny memes/gifs & life-insights, and
health-related reminders or notifications.

#### Crowd Sensing for Population Health

Though participants in _Population Health_ tasks may also actively/voluntarily
engage in the tasks attracted by D&Is for services (i.e., _services and HCI
designs_) above [275], a crucial problem in the tasks does exist –
_participants may not obtain straightforward health benefit compensating their
efforts_ , leading to its unique incentive mechanisms – _recruitment with
monetary incentives_ [42]. Worth mentioning, in most Crowd Sensing for
Population Health practices, the incentive mechanisms (i.e., services and
recruitment) are not used strictly separated; they can be wrapped together to
optimize the incentive effects [276, 43, 277].

  * •

Recruitment with Monetary Incentives - Monetary incentivization is an
intuitive way to quantify and equalize participants’ efforts and benefits,
though some voluntary Crowd Sensing activities also do exist. In practice, for
research or business purposes, mHealth professionals and insurance companies
may consider to promote mHealth apps as tools for groups of interests [278].
The monetary incentives strategies can be further divided into categories as
platform-centric and user-centric methods [53]. The platform-centric methods
refer to that the allocation and adjustment of incentives are charged by the
organizers. For example, based on the game theory [279], the organizers can
lead the task and adjust the strategies by measuring the individual/overall
performance of the participants [280]. The user-centric methods are mostly
conducted in an auction manner, where users bid for the tasks published and
the participants with the lowest bid are dynamically allocated to complete the
sensing tasks [281].

In addition to the above incentive models, there are some works focusing on
the participant selection and incentive allocation problems [56, 52, 282, 57,
283, 284, 235, 236] under certain budgets and data collection
objectives/constraints, since sometimes too straightforward incentive
allocation may lead to biased selection and low retention rate in recruited
populations [285]. Specifically, Xiong _et al._ proposed several participant
recruitment strategies [282, 57, 283, 284] for mobile crowd sensing in either
online or offline manners. Wang _et al._ [235, 236] studied the problem of
participant recruitment and task/incentive allocation in the context of multi-
tasking, where incentives are allocated to the same pool of potential
participants for multiple tasks with shared budgets, via hierarchical data
collection objectives. The same group of researchers also studied to collect
population health-related data from large crowds with non-monetary incentives
in practice [286, 64].

###  IV-B Design & Implementation Issues in Health Surveillance & Data
Collection

Figure 7: The comparison of sensing schemes from granularity and coverage
perspectives

In the Health Surveillance & Data Collection fold, for the objectives of _data
quality_ , sensing schemes and data gathering approaches are the main D&I
issues. As shown in Figure 7, in mHealth Sensing field, either it needs
widespread devices (e.g., mobile devices and social network) with pervasive
coverage among populations, or it needs dedicated devices (e.g., portable
medical devices) for accuracy and professionalism, which is hard to be traded
off, limited by the costs and the accessibility of specific devices. Besides,
there are some trails, surveys, and evaluations approaches in Crowd Sensing
paradigm especially.

#### Personal Sensing for Personalized Medicine

Though the two paradigms sometimes adopt common sensing schemes (e.g.,
wearable devices and mobile devices shown in Figure 7) under some
circumstances, while, for the objectives on numerical accuracy and
longitudinal coverage, the sensing schemes in Personal Sensing practices are
more granularity-oriented.

  * •

Granularity-Oriented Sensing Schemes - To accurately monitor user’s
physical/environmental dynamics in a timely manner, some dedicated and
intensive sensors deployed in medical devices are commonly used in Personal
Sensing practices, such as mobile fall detection devices on elderly care in
daily scenarios [287, 288, 289] and intensive location/maneuvers monitoring
devices in hospital scenarios [290, 291] which are equipped with radar. For
example, Fang _et al._ [292, 293] purposely embedded radio sensor into
wearable devices as a new powerful sensing modality to provide whole-body
activity and vital sign monitoring in clinical, which serves as an example
that specialized sensing schemes provide richer function in Personal Sensing
scenarios.

#### Crowd Sensing for Population Health

To broadly collect health-related data with guarantees of population coverage
and data fidelity, in Crowd Sensing practice, the detail D&Is lie on
_coverage-oriented sensing schemes_ (for population coverage), _trials,
surveys, and evaluations_ (for data collection efficiency and fidelity).

  * •

Coverage-Oriented Sensing Schemes - In Crowd Sensing practices, though many
sensing schemes are the same as those used in the Personal Sensing apps as
shown in Figure 7, while, in order to enable the system to be used in a larger
population coverage, ubiquitous sensing schemes are prevailing in Crowd
Sensing practices, such as social medias (e.g., Facebook and Twitter) [294,
295] and large-scale human mobility data which is not gathered dedicatedly for
health-related purposes [296, 83, 297]. For instance, Choudhury _et al._ used
passive sensed data from social medias to measure and predict the depression
in population [298, 294], even further to discover shifts to suicidal tendency
from content in Reddit [299].

  * •

Trails, Surveys, and Evaluations - In Crowd Sensing data collection process,
it is essential to motivate participants to keep uploading sensing data with
efficiency and fidelity. Typically, trail and survey schemes are for the
efficiency, and data evaluation schemes are for the fidelity. As for _trails
and surveys_ , micro-randomized trials (MRTs) are tools for maintaining and
improving participants’ efficiency by optimizing the combinations of
incentives (e.g., varying levels of monetary incentives, and virtual rewards)
[300, 301, 277]. With MRTs, participants first randomly grouping to collect
data under varying incentives, then in the following sensing loops, the
collected data in the previous round is used to measure which combinations of
incentives are optimal. As for _evaluation schemes_ , they are for enforcing
data fidelity [302]. In specific, once a new round of data _collected_ , but
before accepting the data as convinced, the data fidelity is estimated and
only convinced data is _gathered_ ; according to the estimation, positive or
negative feedback is given to participants to reward/punish them in the
following rounds. An intuitive scheme, named truth discovery [303], is to let
multiple participants finish a same task to find the wrong-data providers
[304]. However, this repeated validation manner cannot be adopted to health-
related data collection since sensitive personal data can only be sensed by
the individual himself/herself. While the trust framework [305] is an
alternative means to solve this. Some measurement methods can be used to
establish a credit rating measurement system for participants, and implement
different acceptance of data contributed by users with different credits, and
varied tasks and incentives are dynamically allocated to enforce participants’
performance in the following sensing rounds [306, 307, 308].

Figure 8: Data Analysis & Knowledge Discovery workflows in three typical
Personal Sensing and Crowd Sensing apps.

###  IV-C Design & Implementation Issues in Data Analysis & Knowledge
Discovery

With respect to detailed sensing objectives listed in Section III-C, we one-
by-one discuss the detail D&I issues in this section. Furthermore, in order to
fully study detailed technical perspectives of the two sensing paradigms,
inspired by the mHealth Personal Sensing framework proposed by Mohr _et al._
[3], we formulate the D&Is of Data Analysis & Knowledge Discovery workflow as
shown in Figure 8.

#### Personal Sensing for Personalized Medicine

Generally, in Personal Sensing, Data Analysis & Knowledge Discovery serves to
mine collected raw data to realize _health status recognition and
interventions_ or _health outcomes and progression predictions_.

  * •

Health Outcomes and Progression Predictions - Due to the fact that most health
problems are determined by multiple pathogenic factors and sometimes progress
slowly, it is not trivial for conventional clinical methods to effectively
predict health outcomes and progressions via sparse clinical records [309].
Personal Sensing data provides rich personalized information to model the
health status of user and predict his/her future health outcomes and
progressions. As shown in Figure 8 (a), after collecting _raw data_ (e.g., GPS
location, microphone signal, and screen status), digital physical and
environmental biomarkers (e.g., places, ambient noises, and app usages) can be
extracted [310, 311]. Then, personal health status modeling and prediction
models analyze individuals’ _clinical status_ and predict health outcomes and
progressions with consideration of longitudinal data both in current and
historical. For instance, in the machine learning era, feature embedding and
deep learning techniques are good tools to solve the challenges in
multidimensional pathogenic factors and long-term disease progression;
specifically, feature embedding techniques (e.g., graph embedding)
automatically learn and extract influential features [312], and deep learning
models (e.g., RNNs, GNNs) could serve as predictors with great performance in
dynamically capturing patterns in temporal and other dimensions [313, 241,
314, 242, 315].

  * •

Health Status Recognition and Interventions - As shown in Figure 8 (a),
according to different health status, the Personal Sensing apps could deliver
varying interventions as healthcare services for users. What’s more, the apps
can further recognize users’ following status for measurement of the
interventions’ effectiveness to refine the strategies and suit the users [316,
317]. As for implementations, activity recognition approaches are helpful for
health status modeling and recognition [318, 319, 320]. Okeye _et al._ [321,
322, 323] proposed multiple-sensors based activity recognition schemes by
extracting knowledge from smart ambiences; and Triboan _et al._ [324, 325,
326, 327] improved the activity recognition methods to be applied in complex
environments in a more real-time and fine-grained manner. Besides, MRTs [328]
are ideal tools to deliver JITAI for patients. As stated in Section IV-B,
analogous to the designs of MRTs in improving the effectiveness of
interventions.

#### Crowd Sensing for Population Health

We discuss two typical applications (i.e., _population health status
measurement_ and _health determinants discovery_) to conclude D&I in Crowd
Sensing applications.

  * •

Population Health Monitoring and Assessments - Intuitively, as shown in Figure
8 (b), once sensing tasks among a group of users are adopted, organizers can
scan the clinical status among populations and achieve assessment of
population status. Furthermore, in the population assessment models, some
techniques (i.e., transfer learning [329]) inspired by some characteristics of
population health problems, such as spatial correlation, help achieve low-
error surveys of entire target group by only monitoring a subset of users. For
example, to investigate a large group of people such as citizens of a country,
Chen _et al._ [64] studied and indicated spatio-temporal correlation of
neighboring regions and proposed to do data inference for the whole map with
limited region samples, which gives insights in operating population health
monitoring in a Crowd Sensing manner.

  * •

Health Determinants Discovery - As shown in Figure 8 (c), the D&Is of
applications on population health determinants discovery differ. Specifically,
in clinical practices, especially for mental health and chronic illness, with
prior knowledge such as clinical diagnosis and EMA, organizers massively
collecting multi-modal data from participants (participants may be divided
into experimental group and control group) and analyze population pattern
among participants’ biomarkers and clinical diagnosis to understand and
discover health determinants; finally, population knowledge serves as
feedback, which benefits to both participants themselves (for health-related
interests) and organizers and researchers (for knowledge about the health
issues researched). From the implementation perspective, large-scale data
analysis methods give great insights on population health knowledge discovery
(e.g., inference and understanding) from Crowd Sensing data. For instance,
machine learning methods such as clustering algorithms are widely used to
classify individuals into groups according to common health-related patterns
[330]. In addition, statistical methods such as statistical inference are also
promising confirmatory tools for understanding and inference on clinical
conclusion than training-based models with confidence intervals on assessment,
which, compared with machine-learning-based methods, is commonly leveraged by
clinical scientists since it is a hypothesis-driven and interpretable manner
[331]. For example, Boukhechba _et al._ [83] used _Social Interaction Anxiety
Scale_ (SIAS) correlation analysis to understand how social anxiety symptoms
manifest in the daily lives of college students; besides correlation analysis,
Huang _et al._ [7] operated a _Least Absolute Shrinkage and Selection
Operator_ (LASSO) linear regression model to infer the causal relationship
between mental health disorders and location semantics.

Though items above could summarize most of the D&Is issues in this step in
Personal Sensing and Crowd Sensing paradigms, there are also some side D&Is
issues for some problems that may exist in the mobile sensing data [332]. For
example, ideally, the input of the data analysis algorithm is continuous and
sufficient, while in mHealth Sensing contexts, the data streams collected may
be sparse and biased due to some technical issues (e.g., operating system’s
restrictions on software running in the background) and varying users’ usage
behaviors (e.g., forgetting to wear the device or run the app); thus
overcoming the insufficiency of data and effective modeling is an urgent
problem to be solved [333, 27]. Additionally, similar side problems include
ways to analyze and understand the relationship between the complex dynamics
of the health and multimodal factors [334], and ways to integrate medical
knowledge into algorithms pervasively and effectively [335].

TABLE III: Summary of two taxonomy systems for mHealth Personal Sensing and Crowd Sensing |  | Sensing Objectives (System I)
---
Sensing Paradigms and D&Is (System II)
| Personalized Medicine
---
Population Health | Personal Sensing | Crowd Sensing
| Sensing
---
Task Creation
&
Participation
| Service provision
---
for personalized
medicine seekers
| Recruitment provision
---
for population
health participants
| Improving user engagement
---
via clinical health services,
gamification and attractions
| Extra motivating participants’
---
performance via recruitment
with monetary incentives
Health Surveillance & Data Collection |  | More focusing on data
---
precision and longitudinal
coverage
| More focusing on data
---
fidelity and population
coverage
Using granularity-oriented sensing schemes |  Using coverage-oriented sensing schemes with trails, surveys, and evaluations
Privacy & Security and Resources Consumption
Data Analysis & Knowledge Discovery |  | Improving mHealth
---
accuracy in risk
prediction and precision
in predictive interventions
| Pursuing depth and
---
coverage of health
monitoring in communities
and statistical power
of mHealth approaches
Leveraging health outcomes and progressions predictions, and health status recognition and interventions |  Leveraging population health assessment and health determinants discovery
Risks and Ethical Issues

##  V Future Directions

In this work, we reviewed the applications and systems of personal sensing and
crowd sensing for personalized medicine and population health, respectively,
and proposed two taxonomy systems for mHealth Sensing systems from the
perspectives of _“Sensing Objectives”_ and _“Sensing Paradigms”_. Here
summarize the two taxonomy systems in Table III. It is obvious that mHealth
Sensing apps in both _Personal Sensing_ and _Crowd Sensing_ paradigms will
continue to be promising research topics to solve both _Personalized Medicine_
and _Population Health_ problems, where some research problems such as data
limitations, data fidelity, privacy & security, risk analysis, and ethical
issues are still not well addressed in mHealth Sensing life cycle. Based on
the proposed taxonomy systems and identified gaps, we foresee the following
research directions in future works.

###  V-A Data Limitation and Data Fidelity

In mHealth contexts, the potential future directions in terms of data could be
solving the research problems of data limitations and data fidelity. First,
the _data limitations_ in time series, as well as between sensor samplings and
system operations [336, 337] lead to discontinuous multimodal data collection
and even loss in mHealth apps [338]. Existing mHealth data analysis works lack
design for handling imperfect heterogeneous data, where transfer learning
techniques may be potential tools [329]. Second, the _data fidelity_ issues
caused by participants’ concealment or deception lead to biased/error data
gathering and misleading/false health conclusions [339]. Thus, besides of
promoting user engagement by incentive strategies, the work of effectively
verifying the fidelity of data uploaded by users is worthy of further study.

###  V-B Privacy and Security Preserving for mHealth

Note that privacy and security have been widely studied in Medical IoTs or
Medical CPSs [171, 172, 340, 176, 177, 178, 341]. Compared to medical IoTs or
medical CPSs deployed at homes or professional clinics, the mHealth sensing
systems leveraging the sensors deployed at ubiquitous mobile devices make the
privacy and security issues even more complicated but lack of studied. To
secure the personal health data from potential leakages, encryption techniques
[342, 343] could be used and optimized for mHealth data management.
Additionally, privacy protection that controls the access of mobile Apps to
some critical information [344, 345] is also required to scale-up mHealth in
societies. In this way, mobile developers frequently need to design and
develop the apps with verification that minimizes data access privileges
subject to the actual needs. Thus, a unified and integrated approach,
combining the data security and privacy controls subject to _principle of
least privilege_ [346, 347, 348] for mHealth sensing, might be a promising
direction for future research.

###  V-C Risks and Ethical Issues in Human-Subject Studies

After-all, the research on mHealth sensing is human-subject studies, where
human involve in-the-loop of scientific studies, data analysis, and
information disclosures, causing potential risks and ethical issues. Though
some works have been done in software developing and data science domains
[349, 350], versus clinical medical practices which pay great attention to
risk analysis and ethical principles [351], the risk analysis and ethical
issues in mHealth area are not properly studied and addressed [261, 262]. For
example, versus medical records and conclusions are drawn under highly
professional processes and stored separately by hospitals’ databases with
strict rules for sharing, the measurements and decisions in mHealth practices
may not be strictly conducted and shared under criterion [260]. Truly, some of
mHealth sensing apps, such as Sensus [4], already include protocol
certification and ethical review components in the system to monitor the whole
life-cycles of mHealth crowd sensing. In the future, scientific study,
protocol management, risk analysis, ethical review, and even prescription
management [184] criteria and techniques should be further studied, especially
for commercially-used mHealth sensing apps and systems.

##  VI Limitations and Conclusions

The mHealth Sensing is a practical approach in modern healthcare domain, which
is being widely used for the objectives on either _(a) personalized medicine
for individuals_ or _(b) public health for populations_. In this work, we
reviewed and summarized mHealth sensing Apps and systems that deployed over
smartphones and commodity ubiquitous devices. Though there are many methods
for reporting systematic reviews (e.g., PRISMA [352]), in this paper, our
review method is mainly intuition-driven and vision-based. We have covered
more than 300 papers and proposing new taxonomy systems that summarize and
categorize existing works in two sensing paradigms (i.e., Personal and Crowd
Sensing) and three stages of the mHealth sensing pipeline in details.

Also, though we have tried our best to cover the important works in this area
and related fields, this survey is still with several limitations. For
example, this work did not include professional medical systems for
medicare/rehabilitation/assisted living purposes, such as medical sensors
[158, 159, 160, 161, 162, 353], Medical IoTs/CPSs [163, 164, 165, 166, 167,
168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182,
183, 184, 185], and medical robots [186, 187, 188, 189, 190, 191].
Furthermore, there have been a number of great works surveying or reviewing
this area and related fields [354, 355, 262, 163, 356, 357, 100, 226, 238,
291, 24, 358, 219, 359, 228, 302, 229, 42, 1, 279, 145], while we have not
compared our taxonomy systems with these works.

To systematically summarize the existing works and identify the potential
directions in this emerging research domain, this work actually presents two
novel taxonomy systems from two major perspectives (i.e., _sensing objectives_
and _sensing paradigms and Designs & Implementations (D&Is)_) that can specify
and classify apps/systems from steps in the life-cycle of mHealth Sensing:
_(1) Sensing Task Creation & Participation_, _(2) Health Surveillance & Data
Collection_, and _(3) Data Analysis & Knowledge Discovery_. Through discussing
the real-world Mobile Sensing apps/systems in the proposed taxonomy systems,
most of the research problems in mHealth Sensing can be formally classified,
and several future research directions are pointed out, targeting to provide
structural knowledge and insightful ideas and guidance for researchers in the
related field.

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