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Background: Type II diabetes mellitus (T2DM) is a common chronic disease. To manage blood glucose levels, patients need to follow medical recommendations for healthy eating, physical activity, and medication adherence in their everyday life. Illness management is mainly shared with partners and involves social support and common dyadic coping (CDC). Social support and CDC have been identified as having implications for people’s health behavior and well-being. Visible support, however, may also be negatively related to people’s well-being. Thus, the concept of invisible support was introduced. It is unknown which of these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial associations with health behavior and well-being when considered together in the context of illness management in couple’s everyday life. Therefore, a novel ambulatory assessment application for the open-source behavioral intervention platform MobileCoach (AAMC) was developed. It uses objective sensor data in combination with self-reports in couple’s everyday life. Objective: The aim of this paper is to describe the design of the Dyadic Management of Diabetes (DyMand) study, funded by the Swiss National Science Foundation (CR12I1_166348/1). The study was approved by the cantonal ethics committee of the Canton of Zurich, Switzerland (Req-2017_00430). Methods: This study follows an intensive longitudinal design with 2 phases of data collection. The first phase is a naturalistic observation phase of couples’ conversations in combination with experience sampling in their daily lives, with plans to follow 180 T2DM patients and their partners using sensor data from smartwatches, mobile phones, and accelerometers for 7 consecutive days. The second phase is an observational study in the laboratory, where couples discuss topics related to their diabetes management. The second phase complements the first phase by focusing on the assessment of a full discussion about diabetes-related concerns. Participants are heterosexual couples with 1 partner having a diagnosis of T2DM. Results: The AAMC was designed and built until the end of 2018 and internally tested in March 2019. In May 2019, the enrollment of the pilot phase began. The data collection of the DyMand study will begin in September 2019, and analysis and presentation of results will be available in 2021. Conclusions: For further research and practice, it is crucial to identify the impact of social support and CDC on couples’ dyadic management of T2DM and their well-being in daily life. Using AAMC will make a key contribution with regard to objective operationalizations of visible and invisible support, CDC, physical activity, and well-being. Findings will provide a sound basis for theory- and evidence-based development of dyadic interventions to change health behavior in the context of couple’s dyadic illness management. Challenges to this multimodal sensor approach and its feasibility aspects are discussed. International Registered Report Identifier (IRRID): PRR1-10.2196/13685
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Original Paper
Social Support and Common Dyadic Coping in Couples' Dyadic
Management of Type II Diabetes: Protocol for an Ambulatory
Assessment Application
Janina Lüscher1, PhD; Tobias Kowatsch2,3, PhD; George Boateng3, MSc; Prabhakaran Santhanam3, MSc; Guy
Bodenmann4, PhD; Urte Scholz1,5, PhD
1Applied Social and Health Psychology, Department of Psychology, University of Zurich, Zurich, Switzerland
2Institute of Technology Management, University of St. Gallen, St. Gallen, Switzerland
3Department of Management, Technology, and Economics, ETH Zurich, Zurich, Switzerland
4Clinical Psychology for Children/Adolescents and Couples/Families, Department of Psychology, University of Zurich, Zurich, Switzerland
5University Research Priority Program “Dynamics of Healthy Aging”, University of Zurich, Zurich, Switzerland
Corresponding Author:
Janina Lüscher, PhD
Applied Social and Health Psychology
Department of Psychology
University of Zurich
Binzmuehlestrasse 14 / Box 14
Zurich, 8050
Switzerland
Phone: 41 446357254
Email: janina.luescher@psychologie.uzh.ch
Abstract
Background: Type II diabetes mellitus (T2DM) is a common chronic disease. To manage blood glucose levels, patients need
to follow medical recommendations for healthy eating, physical activity, and medication adherence in their everyday life. Illness
management is mainly shared with partners and involves social support and common dyadic coping (CDC). Social support and
CDC have been identified as having implications for people’s health behavior and well-being. Visible support, however, may
also be negatively related to people’s well-being. Thus, the concept of invisible support was introduced. It is unknown which of
these concepts (ie, visible support, invisible support, and CDC) displays the most beneficial associations with health behavior
and well-being when considered together in the context of illness management in couple’s everyday life. Therefore, a novel
ambulatory assessment application for the open-source behavioral intervention platform MobileCoach (AAMC) was developed.
It uses objective sensor data in combination with self-reports in couple’s everyday life.
Objective: The aim of this paper is to describe the design of the Dyadic Management of Diabetes (DyMand) study, funded by
the Swiss National Science Foundation (CR12I1_166348/1). The study was approved by the cantonal ethics committee of the
Canton of Zurich, Switzerland (Req-2017_00430).
Methods: This study follows an intensive longitudinal design with 2 phases of data collection. The first phase is a naturalistic
observation phase of couples’ conversations in combination with experience sampling in their daily lives, with plans to follow
180 T2DM patients and their partners using sensor data from smartwatches, mobile phones, and accelerometers for 7 consecutive
days. The second phase is an observational study in the laboratory, where couples discuss topics related to their diabetes
management. The second phase complements the first phase by focusing on the assessment of a full discussion about diabetes-related
concerns. Participants are heterosexual couples with 1 partner having a diagnosis of T2DM.
Results: The AAMC was designed and built until the end of 2018 and internally tested in March 2019. In May 2019, the
enrollment of the pilot phase began. The data collection of the DyMand study will begin in September 2019, and analysis and
presentation of results will be available in 2021.
Conclusions: For further research and practice, it is crucial to identify the impact of social support and CDC on couples’ dyadic
management of T2DM and their well-being in daily life. Using AAMC will make a key contribution with regard to objective
operationalizations of visible and invisible support, CDC, physical activity, and well-being. Findings will provide a sound basis
JMIR Res Protoc 2019 | vol. 8 | iss. 10 | e13685 | p. 1https://www.researchprotocols.org/2019/10/e13685 (page number not for citation purposes)
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for theory- and evidence-based development of dyadic interventions to change health behavior in the context of couple’s dyadic
illness management. Challenges to this multimodal sensor approach and its feasibility aspects are discussed.
International Registered Report Identifier (IRRID): PRR1-10.2196/13685
(JMIR Res Protoc 2019;8(10):e13685) doi: 10.2196/13685
KEYWORDS
social support; common dyadic coping; type II diabetes; dyadic illness management; couples; mobile sensing; multimodal sensor
data; ambulatory assessment application; MobileCoach; study protocol
Introduction
Type II diabetes mellitus (T2DM) is a common chronic disease
of the endocrine system in which the pancreas no longer
produces enough insulin to metabolize blood glucose or the
body becomes less sensitive to insulin (ie, insulin resistance)
[1]. Symptoms include numbness in the hands or feet, excessive
thirst and urination, and nausea [1,2]. Worldwide, 366 million
people suffer from T2DM, which corresponds to 8.3% of the
world population [1]. More than 1 in 4 adults aged 65 years and
older are estimated to have T2DM in the US population,
resulting in 9.4% of the US population [3]. If the trend
continues, 1 of 3 US citizens will have T2DM by 2050 [4]. In
Switzerland, almost 500,000 people suffer from T2DM, which
is approximately 4.9% of the male Swiss population and 4.2%
of the female Swiss population [1,5]. The prevalence rates raise
with increasing age: 15.3% of males and 11.3% of females aged
older than 75 years are diagnosed with T2DM [5]. To manage
blood glucose levels and reduce the risk of diabetes-related
complications (eg, cardiovascular diseases, vision loss, and
amputations), patients need to follow medical recommendations
for healthy eating, physical activity, and medication adherence
in their everyday life [3]. Most T2DM patients take oral
antidiabetic drugs [6]. Diabetes management is, hence, a very
complex endeavor and requires lifelong commitment and
modification of one’s personal lifestyle [4,7]. Evidence suggests
that for married adults, illness management is mainly shared
with their spouses [8,9]. Spousal involvement in patients’
diabetes management may involve social support [10] and
common dyadic coping (CDC) [11,12]. Until now, it is unknown
which of these concepts (ie, visible support, invisible support,
and CDC) displays the most beneficial effects on the diabetes
management in romantic couple’s everyday life when considered
together. Therefore, the aim of this study is to systematically
investigate the effects of social support and CDC on health
behaviors involved in diabetes management (eg, physical
activity, healthy diet, and medication adherence) and well-being
using a novel ambulatory assessment application for
smartphones for the open-source behavioral intervention
platform MobileCoach (AAMC [13]) [14,15] that allows the
objective evaluation of the core study constructs and outcomes
in romantic couples’ everyday lives.
Received Social Support, Health Behaviors, and
Well-Being
Social support describes the provision of resources intended to
benefit a receiver’s ability to cope in times of need [16]. The
most prominent functions of support are emotional (eg,
comforting) or instrumental (eg, practical assistance) [17].
Recipients’ reports of support received can be distinguished
from providers’ reports of support given [18]. The perspectives
of receiver and provider do not necessarily closely correspond,
even when support receipt and provision concern the same
reported support from 2 different persons [19]. There is a
growing body of research reporting positive associations
between received support and health behaviors, such as dieting
or increased physical activity in the general population (eg,
studies by Scholz et al [20], Darlow and Xu [21], Courneya et
al [22], and Molloy et al [23]). In the context of chronic illness,
spouses or partners are mostly the main sources of support [24].
Support from spouses has been associated with positive
outcomes for patients with chronic health conditions, such as
healthier eating habits among diabetic patients [25], increased
health behavior (eg, eating a healthy diet, engaging in physical
activity, and avoiding highly stressful situations) in
cardiovascular patients [26], and decreased risk behaviors in
HIV patients [27]. In this research area, only few studies have
examined social support and health behaviors on a day-to-day
basis (for an exception, refer the studies by Khan et al [7], Iida
et al [28], and Stephens et al [29]). Diabetes symptoms are very
sensitive to lifestyle behaviors and fluctuate daily [30].
Therefore, T2DM offers an ideal context for investigating
associations between partner involvement, well-being, and daily
illness management. For example, focusing on daily processes
revealed that relative to the previous day, spouses’ diet-related
support was associated with increases in patients’ adherence to
diet [29] or that daily spousal support was positively associated
with patients’ daily physical activity [7]. Therefore, it seems
that social support provided by a partner is especially beneficial
for T2DM patients’ illness management.
Research on received social support and coping with stress has
shown that being in a supportive relationship can buffer physical
and psychological effects of illness-related stress in general [31]
and regarding T2DM in particular [28]. In line with this finding,
Stephens et al [29] found that spouses’ diet-related support was
associated with decreases in diabetes-specific distress of T2DM
patients. Contrary to this result, studies on received support and
indicators of well-being oftentimes result in negative
associations between received support and well-being (eg,
studies by Bolger et al [32], Gleason et al [33], and Seidman et
al [34]). As a consequence, Bolger et al [32] introduced the
dyadic concept of invisible social support, which is assumed to
provide the benefits of support receipt without including
potential costs. According to Bolger et al [32], support is
invisible to recipients when the supportive acts occur outside
of their awareness (ie, one partner takes care of unexpected
housework without telling the other) or the recipient may be
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aware of the act but may not code it as support (ie, one partner
purposefully gives advice in an indirect way so as not to draw
attention to the recipient’s distress or his/her inability to deal
with the stressful situation). To date, the few studies that have
investigated invisible support in prospective diary designs (eg,
studies by Bolger et al [32], Shrout et al [35], Maisel and Gable
[36], and Biehle and Mickelson [37]), observational studies (eg,
studies by Howland and Simpson [38] and Girmeet al [39]),
and experimental designs (eg, studies by Bolger and Amarel
[40] and Kirsch and Lehman [41]) have yielded support for
beneficial effects on well-being and encourage further research.
The applicability of invisible support for health outcomes has
been discussed (eg, studies by Kirsch and Lehman [41], Taylor
et al [42], and Westmaas et al [43]). Until now, only 2 daily
diary studies focused on behavioral responses to invisible
support [44,45]. In single-smoker couples, higher invisible
support was associated with less distress, but with more daily
cigarettes smoked after a self-set quit date [44]. In dual-smoker
couples only for men invisible support was associated with less
distress, but not with more smoking after a joint self-set quit
date [44]. Thus, based on these first 2 studies, it seems as if
invisible support can also counteract the negative effects of
visible support on well-being in the context of health behavior
change. However, the results with the health behavior itself
emphasize the need for further understanding the interplay
between invisible support and health behaviors in different
health contexts. Until now, no study was conducted in the
context of chronic illness, and the relevance of invisible support
in T2DM with regard to diabetes-related health behaviors and
well-being needs yet to be demonstrated.
Most studies so far assessed invisible support by calculating a
composite score from 2 self-reports (target person and partner).
On the one hand, researchers assessed invisible support
dichotomously (eg, studies by Bolger et al [32] and Shrout et
al [35]). In this approach, invisible support was coded when the
target person reported no receipt of support, but the support
provider reported provision of support. On the other hand,
researchers calculated continuous invisible support by
subtracting received support reported by the target person from
provided support reported by partners (eg, studies by Biehle
and Mickelson [37] and Lüscher et al [44,45]). Instances in
which the recipient reported receiving more than the provider
reported giving were collapsed to zero. However, these measures
are often criticized not only because invisible support is based
on self-report but also because it is merely calculated and thus
potentially only a hypothetical construct. Therefore, studies in
which invisible support is measured directly, observed, and
coded in everyday life as has already been done in the laboratory
(eg, studies by Howland and Simpson [38] and Girme [39]) are
strongly needed.
Common Dyadic Coping, Well-Being, and Health
Behavior
The usual approach followed by social support researchers is
unidirectional in that support provision of one individual
(usually the healthy one), and support receipt of another
individual (usually the unhealthy one) is in focus. Invisible
support already introduces a dyadic conceptualization in that
both perspectives of partners are usually needed for the
assessment of invisible support. CDC, however, goes beyond
this dyadic implementation of still individual behaviors in that
CDC explicitly focuses on the joint, dyadic efforts a couple
undertakes to overcome challenges and problems (eg, a study
by Bodenmann [11]). There are different approaches to CDC,
but all these conceptualizations share the view that CDC
involves a we approach with regard to stress (eg, studies by
Bodenmann [11], Acitelli and Badr [46], and Kayser et al [47])
and health behaviors (eg, studies by Johnson et al [48], Lewis
et al [49], and Lipkus et al [50]). Thus, CDC explicitly refers
to the couple’s perspective and comprises joint efforts to cope
with a stressor at the couple’s level. In the context of diabetes
management, this could mean that the couple solves all issues
related to the patient’s change of behavior together. Overall,
research on CDC consistently results in positive associations
with well-being and relationship quality (eg, studies by
Bodenmann et al [51] and Traa et al [52]). Results of the
association between CDC and health behavior, however, are
mixed. Some studies report positive associations between CDC
and health behaviors (eg, studies by Johnson et al [48] and
Rohrbaugh et al [53]; for an experimental approach, refer to the
study by Lipkus et al [50]). Along similar lines, within the
context of T2DM, Seidel et al [9] showed that shared
expectations regarding partner's diet-related involvement were
positively associated with diet adherence in male patients.
Benefits of a dyadic approach of patient’s and partner’s illness
representation of T2DM were also documented by Dimitraki
and Karademas [54]. Yet, another experimental study in the
context of diabetes management does not support the hypothesis
that CDC interventions are superior to individual interventions,
albeit the dyadic group proved to be better than the control
group (eg, studies by Trief [55]).
There are several ways in which CDC can be measured. The
usual way of assessing CDC outside the laboratory is through
self-report (eg, using the respective subscale of the Dyadic
Coping Inventory) [56]. At the same time, a coding system for
laboratory observations exists [56]. Another objective alternative
is the assessment of we-talk by counting the use of first-person
plural pronouns (eg, a study by Rohrbaugh et al [57]). Indeed,
in a study by Rohrbaugh et al [57] that distinguished between
we-talk and self-reported CDC, authors found that it was the
objectively measured we-talk, but not the self-reported
communal coping that predicted heart failure symptoms and
general health. Thus, this study sets out to measure CDC with
2 assessment methods combined: self-report and we-talk on a
daily basis in the context of dyadic diabetes management.
Subjective and Objective Ambulatory Assessment by
Mobile Phone Apps
With regard to an in-situ assessment of the theoretical constructs
of this study (ie, visible support, invisible support, and CDC),
mobile phone applications are a powerful tool for several reasons
[58-64]: the widespread use of smartphones and smartwatches
with various sensors and touch-based graphical user interfaces
makes sophisticated assessments of theoretical constructs
appealing and widely applicable. Second, the combination of
sensor data of smartphones and smartwatches (eg, from the
global positioning system sensor or microphone) and their
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proximity to their owners offers the ability to detect useful
contextual information (eg, the geographic position or mood of
the owner). Third, mobile phone applications are scalable,
cost-effective, have low entry barriers, and are applicable to
different target populations. Finally, mobile phone applications
reach people in their everyday life and with an immediacy that
observations using conventional research methods do not have.
In recent years, researchers have increasingly begun to use
smartphones and smartwatches as platforms for the assessment
of health behavior. However, although there exist various mobile
phone applications to monitor behaviors and outcomes related
to diabetes management in general (eg, studies by Sun et al [65],
Schembre et al [66], and Wang et al [67]) and individual facets
of it such as physical activity (eg, studies by Joosen et al [68]
and Bort-Roig et al [69]), nutrition behavior (eg, studies by
Celis-Morales et al [70], Turner McGrievy et al [71], and
Hassannejad et al [72]), well-being (eg, studies by Dubad et al
[73] and Servia-Rodríguez et al [74]) or conflict in couples (eg,
a study by Timmons et al [75]), mobile phone applications that
use objective sensor data in combination with self-reports are
so far not used for the ambulatory assessment of social support
and CDC.
A prominent example of a mobile phone application for the
unobtrusive assessment of natural language and communication
in real life is the electronically activated recorder (EAR) [76].
The EAR collects audio snippets at random times that can be
coded with regard to the content of interest for the respective
studies. For example, there are applications of the EAR with
regard to social support provision in couples coping with breast
cancer (eg, a study by Robbins et al [77]). The focus of the
EAR, however, is on auditory observation only. Novel
sensor–based approaches of affect recognition (eg, studies by
Betella and Verschure [78], Maass et al [79], Venkatesh et al
[80], van der Heijden [81], Chapaneri and Jayaswal [82],
Revathy et al [83], Koolagudi and Rao [84], Heron and Smyth
[85], Spanier [86], and Diener et al [87]) can be used in
combination with appropriate self-report scales such as the
affective slider [78] to better understand outcome parameters
of well-being in the context of diabetes management.
A number of open questions remain from the current literature
on visible and invisible social support, CDC, and its ambulatory
assessment by mobile phone applications that will be addressed
in this study. First, no study has examined the 3 concepts of
visible and invisible social support and CDC in 1 study. Thus,
the unique contributions of these constructs on health behaviors
and well-being have not yet been examined. In particular, with
regard to health behavior change, there is insufficient knowledge
on the effects of invisible social support. The second open
question in the current literature on invisible support and CDC
concerns the assessment in everyday life. Measures of invisible
support are often criticized for being not only merely based on
self-report as measures of visible support but also merely
calculated from independent reports of receipt and provision of
support. Thus, invisible support is potentially only a hypothetical
construct, and studies in which invisible support is measured
directly, observed, and coded in everyday life are strongly
needed. With regard to CDC, most studies so far focused on
either cross-sectional associations or longer-term associations
between CDC and health behavior. Associations in everyday
life using an ambulatory assessment approach have been
neglected so far. Given the assumed importance of invisible
support and CDC for health behaviors involved in diabetes
management and well-being in T2DM patients and their
partners, it is of key importance to assess these constructs in a
reliable and valid objective way in everyday life to further our
knowledge. Third, it is still to be investigated how to design an
ambulatory assessment application for the purpose of this study,
which is not only accepted by study participants in their
everyday situations [79-81] but also delivers high-quality data
streams that are good enough or even comparable with distinct
devices (eg, high-quality microphone for affect recognition from
speech in the laboratory). Fourth, available speech databases
and latest research on affect recognition from speech employ
usually role-taking actors and thus lack natural settings and,
with it, external validity (eg, studies by Chapaneri and Jayaswal
[82], Revathy et al [83], and Koolagudi and Rao [84]). Finally,
multimodal approaches to affect recognition are promising, but
existing research is sparse, and consistent results and approaches
are still to be explored [75]. This study will address all these
open questions and limitations of previous research.
Aims of This Study
The aims of this study are to address the open questions outlined
above. The first aim is to examine the impact of visible and
invisible support and CDC on couple’s dyadic management of
T2DM (ie, health behaviors) and well-being. The second aim
is to use an improved assessment approach of visible and
invisible support and CDC in everyday life, which is based on
observational and self-report data in situ instead of self-report
only. The third aim is closely related to the first and second
aims: that is, to develop an AAMC that allows to record both
multimodal sensor data streams and self-reports related to the
study’s core constructs in situ to better understand visible and
invisible social support and CDC and the relationship between
the recorded sensor data and psychological self-reports. To
better understand outcome parameters of well-being in the
context of diabetes management, the fourth aim is to use a
multimodal affect recognition approach (ie, speech, facial
expression, and heart rate variability) in combination with
self-report scales.
The first aim of the study will be examined in daily life as well
as in an observational setting in the laboratory, whereas the
second and fourth aims refer to the experience sampling phase
of this study. The results of the third aim are finally used to
support the first two and the fourth aims by capturing and
assessing the core study constructs by self-reports and objective
measures during the experience sampling phase. The following
research questions result from these study objectives: (1) What
are the unique contributions of visible and invisible social
support and CDC for diabetes patients’ health-related behaviors
involved in diabetes management (physical activity, diet
adherence, and medication adherence)? (2) What are the unique
contributions of visible and invisible social support and CDC
for indicators of well-being derived from multimodal data
sources and self-reports captured by AAMC in diabetes patients
and their partners?
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Methods
Study Design
This study protocol describes the design of the Dyadic
Management of Diabetes (DyMand) study, funded by the Swiss
National Science Foundation (CR12I1_166348/1). The study
was approved by the cantonal ethic committee of the Canton
of Zurich, Switzerland (Req-2017_00430). To address the aims
mentioned above, this study comprises an intensive longitudinal
design with 2 phases of data collection. The first phase is an
experience sampling phase in romantic couple’s everyday life
(7 days) following an ecological momentary assessment (EMA)
approach [85]; the second phase is an observational study phase
in the laboratory. Figure 1 shows the study design.
Figure 1. Study design.
Sample and Recruitment
The target population of this study are 180 patients with T2DM
and their romantic partners. Inclusion criteria are the medical
diagnosis of T2DM of the target person with prescribed oral
antidiabetic drugs and having a partner of the opposite sex
without diabetes or a psychological disorder who is also willing
to participate in the study. The participating couples should be
in a close, committed relationship for at least 1 year and living
together in 1 household for at least 6 months. Exclusion criteria
include T2DM treatment with insulin, inpatient treatment, shift
work of one or both partners, and insufficient knowledge of the
German language.
Recruitment of patients will take place in several hospitals in
Switzerland. Moreover, couples will be recruited by means of
flyers in medical clinics, hospitals, private practices, pharmacies,
and Schweizerische Diabetes-Gesellschaft (Swiss Diabetes
Society), information provided to physicians who will inform
patients actively about the study, and Web-based forums for
diabetes patients and diabetes-related websites. Furthermore,
we plan to invite patients through radio and television formats
and health magazines.
Detailed Description of the Study
Objective and self-report data from both partners are collected
and assessed throughout the study to allow focusing on effects
of both partners.
Screening and Baseline Assessment
Interested couples will be asked to consent to and then complete
a Web-based questionnaire to screen inclusion and exclusion
criteria and assess sociodemographic information. Moreover,
they will receive first information about the study. Eligible
couples are then invited to the laboratory of the Applied Social
and Health Psychology Group at the University of Zurich for a
baseline assessment. During this session, both partners will
receive comprehensive information about the study, sign the
informed consent form, and fill in a Web-based questionnaire
capturing all constructs of interest at baseline that are not
assessed on a daily basis, but will later serve as control variables.
Control variables are duration of relationship, duration of living
together, duration of T2DM illness, severity of illness, oral
antidiabetic drugs prescribed, illness symptoms, physician’s
recommendations for diabetes management, and relationship
quality of both partners with different dimensions such as
consensus, cohesion, and satisfaction (Dyadic Adjustment Scale)
[86] as well as life satisfaction of both partners (Satisfaction
With Life Scale) [87], technology anxiety [88], familiarity with
mobile phone text messaging applications [89], and experience
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with and usage of smartphones, smartwatches, and step counting
devices [80,88]. They are handed over the study smartphones
(1 for each partner; Nokia 6.1, 2018, Android operating system
9.0) and the study smartwatches (1 for each partner; Polar M600,
Google Wear operating system 2.3) and are instructed how to
use the newly developed AAMC, which will collect the
multimodal sensor data and capture self-reports of both partners
of the participating couples. Moreover, during the 7-day
experience sampling phase, all participants (target persons and
partners) wear triaxial accelerometers at the hip (GT3X+
monitor devices; ActiGraph).
Experience Sampling Phase
The experience sampling sequence starts for all participating
couples on the following Monday after baseline assessment and
ends the following Sunday night to have the same sequence of
days for all participants. Both partners are sent an automatically
generated text message to their own mobile phones on Sunday
evening and again on Monday morning reminding them to put
on the study smartphones, wear the study smartwatches, and
accelerometers to use AAMC directly after getting up.
Participants are instructed to have all devices with them every
day for the 7 days from getting up until going to bed (for a
similar procedure during weekends, refer to the studies by
Robbins et al [90] and Helgeson et al [91]).
The AAMC was developed as an open-source extension of the
existing MobileCoach platform [13] by applying design science
research. The AAMC consists of a smartphone app, a
smartwatch app, and server system built on top of MobileCoach
[92]. MobileCoach is a server-client system that allows both
the collection of sensor and self-report data (eg, for EMA studies
or for health monitoring purposes) and the delivery of health
interventions [14,15]. On the server side, the data collection
and intervention logic are defined (eg, when to collect which
information), whereas short message service text messages and
mobile phone applications for Apple’s iOS and Google’s
Android operating systems are used to actually collect that data
and deliver the interventions. MobileCoach follows the
talk-and-tools paradigm [93]; that is, it provides tools to collect
data and intervene on the one hand (eg integration of Web-based
surveys or the provision of health literacy video clips) and, on
the other hand, to interact with subjects through a digital coach,
also known as chatbot or conversational agent [94], the talk
component. In this study, the digital AAMC coaches PIA
(interacting with the partner with diabetes) and PETE
(interacting with the partner without diabetes) have been
designed to talk to the subjects during the experience sampling
phase with the help of a chat-based interface with predefined
answer options as successfully carried out in previous work (eg,
studies by Kowatsch et al [15,95]).
Design science is a methodology-guided iterative development
of information systems (ISs) and rigorous evaluation of IS
deployments [96,97]. During the build phases, mobile services
were implemented (1) that record multimodal sensor data related
to the study’s core constructs through the study smartwatch,
study smartphone, and a dedicated physical activity device worn
on the hip and (2) that record self-report data of the study’s core
constructs through the smartphone.
The experience sampling is conducted as follows. A 5-min
recording of audio, heart rate, gyroscope, ambient light, and
accelerometer data through the study smartwatch is triggered
when the partners are close to each other and when an acoustic
signal of no silence was detected. The closeness of the partners
is measured by the Bluetooth’s signal strength of the 2
smartwatches. Directly after the 5-min recording, subjects are
notified through an acoustic signal on the study smartphone and
vibration on the study smartwatch to fill out a brief questionnaire
(details are provided below) by the AAMC digital coaches PIA
and PETE from within the chat-based user interface of the
AAMC. If subjects do not start to fill out the brief questionnaire
within 2 min, another acoustic signal and vibration are triggered.
If then, within 2 min, still no response was detected, filling out
the self-report is not possible anymore. During the process of
filling out the questionnaire, a short 3-second video clip of the
participant’s facial expression is recorded with the front-facing
camera of the study smartphone. The following 2 constraints
were added to balance the number of sensors and self-report
recordings and the burden of participants: (1) at least one
recording of 5 min is conducted per hour; that is, if the recording
is not triggered for 45 min as described above (ie, by the
Bluetooth and acoustic signal), a backup recording is done in
the last 15 min of that hour; and (2) the start of 2 recordings has
to be at least 20 min apart from each other. Finally, closeness
between the 2 partners is measured in a regular time interval
with the help of the Bluetooth’s signal strength of the 2
smartwatches during the relevant recording hours.
The relevant recording hours for this experience sampling are
the hours in the morning and evening during the weekdays, that
is, experience sampling days 1 to 5. These hours are defined by
the couples during the onboarding process at the baseline
assessment and can be set from 4 am, 5 am, 6 am, 7 am, 8 am,
and 9 am to 6 am, 7 am, 8 am, 9 am, 10 am, and 11 am for the
morning hours and from 4 pm, 5 pm, 6 pm, and 7 pm to 9 pm,
10 pm, and 11 pm for the evening hours. During the weekend,
that is, experience sampling days 6 and 7, only the early morning
hours and late evening hours are set (eg, from 6 am to 10 pm).
With this procedure, privacy aspects are addressed by primarily
focusing on situations, in which the couples will be spending
time together and thus to reduce the number of audio recordings
during the day of weekdays when chances are higher that
subjects are working, moving around in public places, or visit
or are visited by friends.
The brief questionnaire on the smartphone assesses patients’
received and partners’ provided support, CDC, and affect by
valence and arousal for the past 5 min. Patients report if they
received support from their partners (yes/no) and partners report
if they provided support to their partners (yes/no) in the last 5
min. If the answer is yes, they are asked in what domain they
received/provided support (physical activity, diet adherence,
medication adherence, and other). Moreover, patients report
their perception of CDC with the inclusion of other in the
self-scale and the structure of interpersonal closeness [98]. Items
for the partners will be directly parallel but referring to “your
partner’s diabetes condition.” In addition, the speech recordings
will be transcribed and coded or coded directly from the audio
files with regard to the receipt and provision of social support
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from both partners [90]. Moreover, using the system for coding
dyadic coping (SEDC) [56] and the coding system of we-talk
[53], the audio files will also be coded with regard to CDC. In
doing so, invisible support will be identified when provided
support by the partner is coded from the audio recordings, but
the recipient does not report receipt of support in the subsequent
questionnaire. With this method, we will be able to overcome
the above-mentioned methodological problems of assessing
invisible support in people’s everyday life. At the same time,
reports of receipt of support are the indicators of visible support.
Using these different assessment methods also allows for testing
the unique effects of the 3 constructs: visible support, invisible
support, and CDC without running into problems of completely
shared method variance.
Furthermore, the self-reported valence and arousal dimensions
of affect as experienced in the last 5 min are assessed with the
affective slider [78]. This affect measure is used to assess its
relationship with the multimodal sensor data to derive a novel
digital biomarker for affect based on voice features (eg,
prosody), heart rate, ambient light, gyroscope, accelerometer
data, and facial expressions [75,99]. Both self-reported affect
instrument and the multimodal sensor data linked to affect will
help to deepen our understanding of the outcome well-being.
On every day during the 7-day experience sampling phase,
participants will also be asked by PIA and PETE from within
the chat-based user interface of AAMC to complete a short
end-of-day diary with more comprehensive questions on social
support and CDC, healthy eating, medication adherence, and
well-being of both partners to cover also the times of the day
that are not captured by the random audio recordings and
subsequent brief self-reports. Measures in the end-of-day diary
are adapted from the studies by Bolger et al [32] received and
provided social support is assessed by asking patients “Today,
I received emotional/instrumental support from my partner”
and by asking partners “Today, I provided
emotional/instrumental support to my partner.” Emotional and
instrumental support will be briefly defined for participants.
Moreover, patients will report their perception of CDC with the
items (1) “When you think about problems related to your
diabetes condition today, to what extent did you view those as
‘our problem’ (shared by you and your partner equally) or
mainly your own problem?” with a bipolar response scale from
1= today completely my own problem to 6= today always our
problem and (2) “When today a problem related to your diabetes
condition arose, to what extent did you and your partner work
together to solve it?” with a response scale from 1= today not
at all to 6= today very much. Both items are adapted from
Rohrbaugh et al [57] to a daily basis. Items for the partners will
be directly parallel but referring to “your partner’s diabetes
condition.” For dietary adherence, patients report the extent to
which they had followed a recommended diet. Medication
adherence is assessed with the Medication Adherence Rating
Scale [100] adapted to a daily basis. Thereof, for dietary
adherence and medication adherence, a dichotomous measure
(adherence to recommendations yes-no) results. Finally,
psychological well-being is assessed with the short form of the
Positive and Negative Affect Schedule [101] and with the
affective slider [78] on a daily basis of both partners. During
this end-of-day diary and similar to the experience sampling
procedure, a short 3-second video clip of the participant’s facial
expression is recorded with the front-facing camera of the study
smartphone. This recording together with the additional
multimodal sensor data collected over the course of the day will
be used to investigate the relationship between self-reported
measures of affect and well-being and the multimodal sensor
data [99].
In addition to the experience sampling and the end-of-day diary,
physical activity is measured continuously throughout the 7
study days with the study smartwatch and a triaxial
accelerometer worn on the hip (GT3X+ monitor devices). To
parallel recommendations for physical activity [102], a measure
of the minutes of moderate-to-vigorous physical activity will
be created by summing the minutes of moderate exercise and
vigorous exercise from the accelerometer data. By doing so, a
dichotomous measure (adherence to recommendations yes-no)
results. Furthermore, the ActiGraph is used to validate the
physical activity data of the study smartwatch with the overall
objective to assess the need of a device that measures physical
activity in addition to the accelerometers integrated into the
smartwatches. In the best case, consistent results among the
different devices would lead to a removal of the dedicated device
and thus to decrease the burden of subjects in future EMA
studies or health interventions.
Observational Study Phase
After the 7-day sequence, participants will return to the
laboratory to hand in the study smartphones, smartwatches, and
accelerometers. Moreover, couples will then participate in the
second part of the study, the observational study, and complete
a final questionnaire on the AAMC on their study smartphones.
The observational study examines visible and invisible support
and CDC by analyzing couple’s videotaped discussion about
diabetes-related concerns during a 10-min discussion.
Using the same procedure as was used by Dagan et al [103] and
Badr et al [104], T2DM patients and their partners will be asked
to list their T2DM-related and illness management–related
concerns and select one that is causing them considerable
distress. Next, they will be invited to discuss the issue with their
partner for about 10 min in a videotaped session. The task will
be guided by a psychologist who will leave the room during the
discussion. The underlying idea is that the discussions will
capture how couples talk about T2DM-related concerns. During
the discussion, each partner will wear a smartwatch as it collects
various sensor data similar to that in the ambulatory setting for
the experience sampling phase. Following the discussion, both
partners will report their perception of the discussion, and both
partners will rate the discussion in terms of the degree to which
it has been typical of their discussions at home, how helpful it
was, and how it made them feel. Furthermore, both partners
will complete measures on how much they felt supported and
how much they were themselves providing support. This allows
the assessment of invisible support by coding provision of
support and self-reported receipt of support [38,39]. Also, they
will each complete the Affective Slider self-report, assessing
the valence and arousal dimensions of their affect over the last
10 min of the discussion. The videotaped discussions are
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subsequently coded by trained observers for visible and invisible
support transactions and CDC. For this, a codebook will be
developed based on previously published work for visible and
invisible support [38,39,104,105]. We will use the SEDC [56]
for coding CDC. Moreover, 2 trained blinded coders, showing
high interrater reliability (kappa) after training, will review the
videotaped discussions for the support provided (observer-rated
support) and dyadic coping strategies independently. This
procedure will not only consider both perspectives of social
support of a couple but also an observer perspective as suggested
by Dunkel-Schetter et al [19].
Finally, couples will complete separately from each other a
Web-based survey on technology acceptance constructs with
regard to AAMC such as perceptions of enjoyment, ease of use,
usefulness, and the intention to interact with the digital AAMC
coaches PIA and PETE [80,94,106-109]. In addition, consistent
with previous research on technology acceptance, 7-point Likert
scales ranging from strongly disagree (1) to strongly agree (7)
will be used. To assess the attachment bond of the participants
with the digital coaches PIA and PETE and also the shared
understanding between them and subjects with respect to the
EMA goals and tasks, a short version of the working alliance
inventory for technology is adopted from previous work
[110-113]. In particular, we will use the Session Alliance
Inventory by Falkenstro m and Hatcher [113] with 6 items
because of the short duration of the EMA study with a 6-point
response scale ranging from not at all (1) to completely (6).
Finally, subjects are asked to indicate potential improvements
related to the AAMC. All the couples will receive a
compensation of CHF 100 for their time and travel expenses.
Statistical Analysis
The main research aims of this study refer to between-person
associations of visible support, invisible support, CDC, patient’s
diabetes-related health behavior, and well-being of both partners
using a dyadic approach to account for the interdependence
among couple members using a 2-level statistical model for
distinguishable dyads as indicated for patient-partner dyads
[114]. The main analyses will be correlations and multiple
regression analyses, which will be performed in SPSS and R.
For the diabetes-related health behaviors, which relate to
physical activity, diet, and medication adherence, we will
generate a daily composite score, indicating the meeting of the
recommendations for all 3 behaviors together ranging from 0
(meeting none of the recommendations) to 3 (meeting all of
them). Moreover, we will also be able to analyze associations
between predictors and the different behaviors assessed
continuously in separate analyses. The idea of the composite
score, however, takes into account that the real-life assessment
method we chose to capture invisible and visible support and
CDC using objective measures might result in highly
ecologically valid and reliable measurements, but potentially
in a rather low frequency of these predictors for the different
diabetes-related health behaviors. With regard to well-being,
we will consider the affective valence and arousal assessed
during the experience sampling sequences or the mean scores
of positive and negative affect from the end-of-day assessments.
On a more exploratory level, we will also analyze day-to-day
within-person and within-couple associations in further analyses
using multilevel modeling. But because of the novelty of our
approach and the rather short time frame of 7 days, this will not
be the main focus.
To assess the relationship between the sensor data and
self-reports, machine learning is applied, which is carried out
in several steps. First, preprocessing of the raw sensor data
involves feature extraction, feature scaling, feature selection,
and dimensionality reduction. The resulting features derived
from sensor data will be tested in machine learning models to
predict self-reported affect and well-being. Second, the data
will be split into training and test datasets to assess how derived
algorithms generalize to new data [115]. The training dataset
will also be split into subsets, where a k-fold cross-validation
will be applied. The performance of the resulting model will
then be evaluated using the test data set. This procedure will be
repeated for various learning algorithms (eg, random forest,
support vector machines, naive Bayes, recurrent neural
networks, and feedforward neural networks). After comparing
the performance across algorithms, the best overall model will
be selected. We expect to produce a model that efficiently
predicts affect and well-being using a multimodal compared
with a unimodal approach as outlined in previous work on
affective computing [99].
Power Analysis and Sample Size
The sample size was calculated based on Cohen [116] and using
the G*Power program [117] to secure adequate power for the
primary outcomes. There are no meta-analyses available for the
associations between visible support, invisible support, and
CDC with health behaviors and indicators of well-being.
Moreover, studies reporting results from diary studies use
unstandardized effects. Thus, we base our power calculation on
data from previous studies on these associations, but we are
aware that the data basis is somewhat unsatisfactory to make
strong conclusions about the expected effect sizes. Previous
studies reported varying effect sizes for visible received support
on health behavior (eg, r=0.29-0.34) [20,23]. There are only 2
studies so far that examined the association between invisible
support and health behavior (eg, smoking) [44,45]. In these 2
studies, however, no standardized effect sizes are available
because of the focus of within-person effects. In the case of
visible and invisible social support and their relations to
well-being, we draw on the experimental evidence available
[40]. In a series of 3 experiments, Bolger and Amarel [40]
demonstrated that visible support compared with a no-support
control group was related to increases in distress (d=0.27 and
d=0.66, ranging from small to medium effect sizes). Invisible
support, in contrast, leads to lower distress (d=0.63 and
d=1.09, indicating medium to large effect sizes). The difference
between the effects of invisible and visible support resulted in
a large effect (d=1.09). Thus, medium effect sizes for the
associations between visible and invisible support and indicators
of well-being will be expected. In the case of CDC, previous
studies report effect sizes with behavior of r=0.20 with exercise
adherence, r=0.20 with dietary adherence in a sample of diabetes
patients [48]. Moreover, in the domain of well-being, effect
sizes range from r=0.20 to r=0.33 (eg, a study by Badr et al
[118]).
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As we will not only address bivariate associations between our
predictors and criteria but aim at comparing effects between
our predictors, our calculation of the power is based on a 2
dependent Pearson r’s analysis with a common index. To the
best of our knowledge, no published data on the intercorrelations
of visible support, invisible support, and CDC are available.
Data from our own research with smoking-nonsmoking couples
(excluding CDC) resulted in an association of r=0.25 for
visible and invisible support. To detect a significant association
with a continuous outcome at P<.05 with a power of
1beta=0.90, and assuming a small effect size of r=0.2, for
health behavior and visible support, r=0.2, for health behavior
and invisible support and an intercorrelation of r=0.25 between
visible and invisible support, the required sample size is 164.
Because effect sizes for CDC tended to be higher, the study will
be powered for the smaller effect sizes of support. Previous
studies applying intensive longitudinal designs resulted in very
low rates of dropout even across a longer period (ie, 10%)
[119-121]. This can be explained with a high commitment in
couples willing to participate in these kinds of studies. On the
basis of this experience, we will add 10% to the calculated
sample size to account for potential dropout. This results in a
total required number of 180 couples.
Analyses of the relationship between multimodal sensor data
and self-reported data on affect and well-being are conducted
with the help of machine learning. We estimate that in the best
case, we will have an average of 10 completed self-reports per
day for all 7 days resulting in 25,200 samples (360
individuals×10 self-reports×7 days). It is highly likely to have
missing data if subjects do not complete the self-reports.
Therefore, in a worst-case scenario with only 1 self-report
completed per day and a corresponding 90% missing data, there
will be 2520 observations left to use to train the machine
learning algorithm. Owing to the fact that there exists no widely
accepted sample size calculation method for machine learning
approaches, the sample size of 360 individuals with up to
multiple measurements on 7 consecutive days (eg, at least 2520
observations only from the end-of-day diary) lies above related
work (eg, studies by Wahle et al [60] and Timmons et al [75])
and thus is assumed to be adequate for the purpose of this study.
Data
In line with the open research data initiative of the Swiss
National Science Foundation, the anonymized data of the study
will be made public in a noncommercial database for replication
purposes of the analyses, additional data analyses by any third
parties (eg, other research groups), and quality control purposes,
given that there are no ethical or legal restrictions.
Results
The AAMC was designed and built until the end of 2018 and
internally tested in March 2019. In May 2019, the enrollment
of the pilot phase began. The data collection of the DyMand
study will begin in September 2019, and analysis and
presentation of results will be available in 2021.
Discussion
The impact of social support and CDC on health behavior
change and well-being has attracted researchers’ interests for
some time. Researchers found that social support and CDC are
associated with benefits for health behaviors and well-being
(eg, studies by Scholz et al [20], Stephens et al [29], Johnson
et al [48], Bodenmann et al [51], Rohrbaugh et al [53], and
Uchino et al [122]) but often also with costs and harmful
consequences (eg, studies by Bolger et al [32], Gleason et al
[33], Seidman et al [34], Westmaas [43], and Trief [55]). For
further research and practical reasons, it is crucial to identify
which types of social support and CDC are beneficial and which
are potentially harmful for health behaviors and indicators of
well-being. Diabetes management is an ideal field to tackle this
task. T2DM is a widespread disease and can be treated and
managed by following a healthy meal plan, regular physical
activity, and taking medications to lower blood glucose levels.
Thus, patients’ education and self-care practices are important
aspects of T2DM management that help patients to stay healthy.
The social environment has been found to be highly influential
in the illness management process [10,11] although effects of
social support and CDC on health behavior change and
well-being in the context of T2DM management are not yet
well understood. This study is the first to systematically
investigate couples’ dyadic illness management by investigating
visible and invisible support and CDC in T2DM patients and
their partners in daily life by applying an experience sampling
approach and an observational approach on the basis of the new
open-source behavioral intervention platform MobileCoach
[14,15].
This combined experience sampling and observational approach
is highly relevant for the following reasons: analyzing visible
and invisible support and CDC in daily life has thus far not been
done with a focus on a dyadic perspective by considering T2DM
patients and their partners. Furthermore, so far it is unknown
which of these concepts displays the most beneficial associations
with well-being and health behavior when considered together.
Moreover, measurement of these constructs in people’s everyday
life is by self-report only. Current technical developments allow
using alternative, more objective operationalizations of these
constructs and solve problems linked to self-report instruments.
However, with the method applied in this study, we will not be
able to capture all forms of invisible social support. For example,
all kinds of nonverbal support behaviors, such as hugging as a
form of emotional support, will not be detected. Nonetheless,
we consider this assessment method innovative and
advantageous to mere self-report measures of invisible support.
Furthermore, this study will not only substantially advance the
knowledge in the area of couple’s dyadic management of T2DM
but also on the important question of which supportive or coping
acts are positively related to health behavior change and
well-being. Therefore, this will provide a sound basis for the
development of theory-based and evidence-based dyadic
interventions to change health behavior.
Furthermore, the technical contribution of this project, that is,
the enhancements of the MobileCoach platform with its newly
developed modules (AAMC) and its capabilities to capture
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subjective self-report data and objectively physical activity and
affect through a multimodal sensor fusion approach, will be
made open source under the research and industry-friendly
Apache 2 license [123]. Thus, we expect high adoption rates
and further developments of the AAMC by public institutions,
business organizations, and interdisciplinary research teams in
the field of ISs, computer science, health psychology, and
behavioral medicine. The AAMC is generic in that it may not
only be suitable for diabetes management but also for related
diseases such as obesity, hypertension, or mental health
disorders in which self-reports, physical activity, and affect
detection take over a key role for diagnosis and health
intervention designs. Moreover, given a sufficient degree of
classification accuracy in using sensed and EMA data to predict
affect and well-being, these models could then be used to help
reach vulnerable individuals early and provide appropriate
just-in-time adaptive interventions [124], respectively.
There are several issues that might raise questions on the
feasibility of this study. First, speech recordings have repeatedly
been questioned with regard to research ethics because it might
happen that people not involved in the research study are being
recorded too or that participants’ privacy is endangered. With
regard to protecting participants’ privacy, for example, all
participants have the opportunity to listen to their audio files
and request their deletion without giving any reason and without
anyone else listening to them as already applied by Robbins et
al [77,90]. Moreover, with regard to the recording of other
people than the participating couple who did not provide
informed consent, the following steps will be taken: First,
participants will be advised to wear a small badge signaling that
audio recording might happen as applied by Robbins et al
[77,90]. Second, identity of anyone else than the partner will
not be possible to be detected by the study personnel. Thus,
anonymity will be granted.
Every study applying intensive longitudinal data assessment in
people’s everyday life faces the challenge to potentially
overburden participants resulting in high rates of refusal to
participate or high attrition rates. There are studies using an
intensive data assessment in diverse populations, demonstrating
that studies similar to this one are feasible (eg, a study by
Helgeson et al [91]); adolescent diabetes patients completed a
3- to 5-min questionnaire on the palm pilot every 2 hours
throughout the day over a 2-day weekend, without increasing
participants burden to the point of noncompliance). Another
important challenge of this study regards the time-consuming
and labor-intensive process involved in handling the large
volumes of audio and video data. It is possible that as technology
further develops, there will be the opportunity to make use of
advanced speech recognition software; however, such software
is not yet sophisticated enough to pick up on fineness of social
interactions in romantic couple’s everyday life.
Using AAMC methodology will make a key contribution with
regard to the objective operationalizations of social support,
CDC, and physical activity, and thus, we will be able to provide
detailed characterization of romantic couple’s communication
about their dyadic diabetes management in daily life. To deepen
the understanding of when social support and CDC are
particularly effective, the data recorded with the AAMC will
also be used to detect affect by a multimodal sensor fusion
approach. The results of this study will provide a sound basis
for the theory- and evidence-based development of dyadic
interventions to change health behavior in the context of
couple’s dyadic illness management. Implications may include
exploring opportunities for the use of the AAMC methodology
and inform other areas of couple’s everyday illness management
of other chronic illnesses.
Acknowledgments
The project, the first author JL, and the authors GB and PS were funded by the Swiss National Science Foundation
(CR12I1_166348/1).
Authors' Contributions
US and G Bodenmann are the principal investigators of the study. US and JL developed the study design together. The design
of the EMA logic (Bluetooth and acoustic pattern triggered recordings of the multimodal data sources) and the incorporation of
the multimodal approach to affect detection was designed by TK, G Boateng, and PS. JL coordinates the study. JL drafted the
manuscript. US, TK, G Boateng, PS, and G Bodenmann contributed to the manuscript. All authors read and approved of the final
manuscript.
Conflicts of Interest
None declared.
Multimedia Appendix 1
Existing peer-review reports from the Swiss National Science Foundation.
[PDF File (Adobe PDF File)481 KB-Multimedia Appendix 1]
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Abbreviations
AAMC: ambulatory assessment application for the open-source behavioral intervention platform MobileCoach
CDC: common dyadic coping
EAR: electronically activated recorder
EMA: ecological momentary assessment
IS: information system
SEDC: system for coding dyadic coping
T2DM: type II diabetes mellitus
Edited by G Eysenbach; submitted 12.02.19; peer-reviewed by R Pryss, TT Luk, YH Lin; comments to author 11.04.19; revised version
received 05.06.19; accepted 29.06.19; published 01.01.19
Please cite as:
Lüscher J, Kowatsch T, Boateng G, Santhanam P, Bodenmann G, Scholz U
Social Support and Common Dyadic Coping in Couples' Dyadic Management of Type II Diabetes: Protocol for an Ambulatory
Assessment Application
JMIR Res Protoc 2019;8(10):e13685
URL: https://www.researchprotocols.org/2019/10/e13685
doi: 10.2196/13685
PMID:
©Janina Lüscher, Tobias Kowatsch, George Boateng, Prabhakaran Santhanam, Guy Bodenmann, Urte Scholz. Originally published
in JMIR Research Protocols (http://www.researchprotocols.org), 05.10.2019 This is an open-access article distributed under the
terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Research Protocols, is
properly cited. The complete bibliographic information, a link to the original publication on http://www.researchprotocols.org,
as well as this copyright and license information must be included.
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... Social support among couples and CDC in chronic disease management have been shown to have mostly positive effects on emotional well-being [23,32,53,60], and result in healthier eating habits among diabetes patients [46]. Consequently, it is of interest to better understand couples' dyadic interactions in-situ, for example, in couples' management of diabetes in daily life [35,42] as they could enable the development and delivery of behavioral interventions to, for example, improve physical activity, diet, and medication adherence. ...
... This work builds upon a study protocol published in 2019 [42] and it is an extension of our prior work [16,20] and it includes a more detailed description of our DyMand system and its real-world deployment and evaluation. This paper is organized as follows. ...
... In developing DyMand, experts from the field of computer science, information systems, and health psychology used justificatory knowledge from prior work [19,32,42,46,58] about social support, CDC, health behavior, and emotional well-being to derive a list of design specifications that are important for collecting corresponding data in-situ, in the context of chronic disease management. We describe the specifications. ...
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Dyadic interactions of couples are of interest as they provide insight into relationship quality and chronic disease management. Currently, ambulatory assessment of couples' interactions entails collecting data at random or scheduled times which could miss significant couples' interaction/conversation moments. In this work, we developed, deployed and evaluated DyMand, a novel open-source smartwatch and smartphone system for collecting self-report and sensor data from couples based on partners' interaction moments. Our smartwatch-based algorithm uses the Bluetooth signal strength between two smartwatches each worn by one partner, and a voice activity detection machine-learning algorithm to infer that the partners are interacting, and then to trigger data collection. We deployed the DyMand system in a 7-day field study and collected data about social support, emotional well-being, and health behavior from 13 (N=26) Swiss-based heterosexual couples managing diabetes mellitus type 2 of one partner. Our system triggered 99.1% of the expected number of sensor and self-report data when the app was running, and 77.6% of algorithm-triggered recordings contained partners' conversation moments compared to 43.8% for scheduled triggers. The usability evaluation showed that DyMand was easy to use. DyMand can be used by social, clinical, or health psychology researchers to understand the social dynamics of couples in everyday life, and for developing and delivering behavioral interventions for couples who are managing chronic diseases.
... Furthermore, social support from partners in chronic disease management has been shown to either have positive or negative effects on the emotional well-being of patients [15,50,76]. Because of the importance of emotions among couples, researchers are working towards understanding the emotional processes that take place in intimate relationships (e.g., [34,90]) and the link between emotions and social support in couples' dyadic management of chronic diseases [64]. Consequently, being able to automatically recognize each partner's emotions could enable the research of social and health psychologists, and also inform the development of dyadic interventions (where partners are both involved e.g., [54]) to improve the emotional well-being, relationship quality, and chronic disease management of couples. ...
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Couples' relationships affect the physical health and emotional well-being of partners. Automatically recognizing each partner's emotions could give a better understanding of their individual emotional well-being, enable interventions and provide clinical benefits. In the paper, we summarize and synthesize works that have focused on developing and evaluating systems to automatically recognize the emotions of each partner based on couples' interaction or conversation contexts. We identified 28 articles from IEEE, ACM, Web of Science, and Google Scholar that were published between 2010 and 2021. We detail the datasets, features, algorithms, evaluation, and results of each work as well as present main themes. We also discuss current challenges, research gaps and propose future research directions. In summary, most works have used audio data collected from the lab with annotations done by external experts and used supervised machine learning approaches for binary classification of positive and negative affect. Performance results leave room for improvement with significant research gaps such as no recognition using data from daily life. This survey will enable new researchers to get an overview of this field and eventually enable the development of emotion recognition systems to inform interventions to improve the emotional well-being of couples.
... This review provides only a snapshot of a rapidly evolving research area. The studies identified included, for instance, some study protocols for intervention studies that seem to be currently underway (Wittmann et al., 2017;Lüscher et al., 2019). Future reviews should also overcome the distinction between DC and spousal support literature. ...
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Objective: Chronic physical illness affects not only patients but also their partners. Dyadic coping (DC)—the ways couples cope in dealing with a stressor such as chronic illness—has received increased attention over the last three decades. The aim of the current study was to summarize the state of research on DC in couples with chronic physical illnesses. Methods: We conducted a systematic review of qualitative, quantitative, and mixed-methods studies published between 1990 and 2020, assessing DC in couples affected by severe physical illnesses. We used DC and related search terms for the literature search in Psycinfo, Psyndex , and Medline . Five thousand three hundred thirty studies were identified in three electronic databases and 49 of these were included in the review (5,440 individuals reported on 2,820 dyads). We excluded studies on cancer, cardiovascular disease, and multiple sclerosis because of existing reviews in the respective fields. Half of the studies included were on diabetes. Other studies were on arthritis, chronic obstructive pulmonary disease (COPD), cystic fibrosis, human immunodeficiency virus (HIV), Huntington's disease, lupus erythematosus, Parkinson's disease, renal diseases, stroke, and endometriosis. Two raters extracted data using a predefined protocol, including study quality. Results were collated in a narrative synthesis organized by illness and DC operationalization. Results: Overall, DC was associated with beneficial outcomes in physical health, well-being, and relationship satisfaction. Differential effects became apparent for certain chronic conditions potentially depending on certain disease characteristics, such as early-onset, sudden-onset, or life-threatening conditions. Conclusion: Facing challenges together as a couple seemed indispensable for adapting to a diverse range of demands related to chronic illnesses with some specific demands of particular chronic diseases. There is a need for the development of truly dyadic interventions with an eye on the specific challenges of the various chronic conditions.
... The intervention was developed with the open-source software platform MobileCoach [67,83,84], which has already been used successfully for various clinical and public health interventions [17,68,[77][78][79]85,86] and ecological momentary assessments [87][88][89]. MobileCoach is available under the academia-and industry-friendly open-source Apache 2.0 license. MobileCoach-based interventions are delivered via SMS text messages, and via mobile apps for the Android and iOS operating systems. ...
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... While observations in the lab have been conducted in previous studies on support provision and relationship health (e.g., Lawrence et al., 2008;Jensen et al., 2013), a future way to go could lie in naturalistic observations of support instances in daily life via audio recordings (cf. Lüscher et al., 2019), using an electronically activated recorder (EAR; Mehl et al., 2001). ...
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Intimate partners are an important source of support when pursuing health goals. A vast amount of literature documents the role of social support in alleviating recipients’ distress and facilitating health behaviors. Less studied is the phenomenon that providing support may entail a benefit for the provider, particularly in the context of health behavior change. In the present study, we investigated whether providing social support in daily life would be associated with more health behavior, and emotional and relational well-being that same day, using a sample of romantic couples aiming to become more physically active. Ninety-nine inactive and overweight heterosexual romantic couples (=198 individuals) participated in this dyadic daily diary study. Both partners reported on the provision of social support, positive and negative affect, and relationship satisfaction in electronic end-of-day diaries across 14 consecutive days. Moderate-to-vigorous physical activity (MVPA) was objectively assessed via triaxial accelerometers (Actigraph GT3X+). Using the Actor-Partner Interdependence Model (APIM), dyadic data analyses indicated that providing support to the partner was associated with higher own MVPA, more own positive affect, less own negative affect, and more own relationship satisfaction (actor effects), over and above the effect of support provision on outcomes in the other partner (partner effects). The present findings suggest that the provision of daily social support in couples is strongly associated with enhanced well-being not only at a personal level but also at a relational level. Providing social support may also serve the function of relationship maintenance. Thus, shifting the focus away from the recipient to examine beneficial effects of social support in providers is highly relevant. Future research should address the question of when, why, and how giving support is beneficial.
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Introduction: Companionship (i.e., enjoyable shared activities) is associated with higher emotional and relational well-being. However, the role of companionship for emotional well-being and relationship satisfaction in older couples' everyday life is not well understood. This article studies time-varying associations of companionship with emotional and relational well-being as older couples engage in their everyday life. Methods: Participants provided three data points a day over 7 days using electronic surveys that were simultaneously completed by both partners. A total of 118 older heterosexual couples reported momentary companionship, positive and negative affect, and closeness. Data were analyzed using an intensive longitudinal dyadic score model. Results: Couples with higher average companionship showed lower overall negative affect, more overall positive affect, and higher overall closeness. During moments of elevated momentary companionship, partners reported more positive affect, less negative affect, and higher closeness. Regarding between-couple partner differences, i.e., when the female partner's momentary companionship was higher on average than the male partner's momentary companionship, the female partner also showed less negative affect, more positive affect, and higher closeness than the male partner. During moments in which the female partner's momentary companionship was higher than the male partner's momentary companionship, the female partner showed less negative affect, more positive affect, and higher closeness than the male partner. Discussion: Older couples show a consistent link between companionship and emotional well-being and closeness in everyday life emphasizing the importance of studying companionship in close relationships.
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