Hintergrund: Die Einbindung von Smartphones in den Alltag ermöglicht es, longitudinale Echtzeit-Daten aufzuzeichnen, die keine aktive Eingabe der Nutzer benötigen. Mithilfe der vorliegenden Studie soll gezeigt werden, dass das aktuelle ­Befinden (Stimmung, Antrieb, Stress) mit Smartphone-Nutzungsvariablen assoziiert ist. Methoden: An der Studie nahmen 157 Studierende teil, welche für 8 Wochen die App Insights auf ihrem Smartphone installierten. Insightszeichnete das Smartphone-Nutzungsverhalten (z.B. Gesamtnutzungsdauer, Anrufdauer, Anzahl der SMS, Facebook-Nutzung) auf und erfasste täglich mittels Selbstbericht Stimmung, Antrieb und Stress. Ergebnisse: In 3 Mehrebenenmodellen wurde das aktuelle Befinden über Smartphone-Nutzungsverhalten vorhergesagt. Die Ergebnisse demonstrieren, dass Stress negativ mit der Anzahl der SMS (–3,539, SE = 0,937) und positiv mit der Anrufdauer (0,018, SE = 0,937) assoziiert ist. Stimmung ist negativ mit der Gesamtnutzungsdauer (–0,019, SE = 0,004) und der Anrufdauer (–0,016, SE = 0,007) verbunden. Ebenso ist der Antrieb negativ mit der Facebook-Nutzung (–0,127, SE = 0,041) korreliert. Diskussion: In zukünftigen Studien sollte der Kausalität des negativen Zusammenhanges zwischen ­Befindlichkeitsparametern und der Smartphone-Nutzung nachgegangen werden. Schlussfolgerung: In Zukunft könnte passives Smartphone-Tracking eingesetzt werden, um standardisiert Verhaltensdaten von Personen mit psychischen Problemen zu sammeln. Aufgrund der immanenten Gefahr des Datenmissbrauchs sind ethische, rechtliche und berufspolitische Leitlinien zu entwickeln.

1.
Alvarez-Lozano
J
,
Osmani
V
,
Frost
M
,
Bardram
J
,
Faurholt-Jepsen
M
,
Kessing
LV
.
Tell me your apps and I will tell you your mood: Correlation of apps usage with Bipolar Disorder State
.
Proc 7th Int Conf PErvasive Technol Relat to Assist Environ
.
2014
;
1
.
2.
Baumeister
H
,
Grässle
C
,
Ebert
DD
,
Krämer
LV
.
Blended Psychotherapy – verzahnte Psychotherapie: Das Beste aus zwei Welten? PiD -
.
Psychother Dialog
.
2018
;
19
(
04
):
33
8
. 1438-7026
3.
Harald
B
,
Gordon
P
.
Meta-review of depressive subtyping models
.
J Affect Disord
.
2012
Jul
;
139
(
2
):
126
40
.
[PubMed]
0165-0327
4.
Bengio
Y
,
Courville
A
,
Vincent
P
.
Representation learning: a review and new perspectives
.
IEEE Trans Pattern Anal Mach Intell
.
2013
Aug
;
35
(
8
):
1798
828
.
[PubMed]
0162-8828
5.
Bender
R
,
Lange
S
.
Adjusting for multiple testing—when and how
.
J Clin Epidemiol
.
2001
Apr
;
54
(
4
):
343
9
.
[PubMed]
0895-4356
6.
Ben-Zeev
D
,
Scherer
EA
,
Wang
R
,
Xie
H
,
Campbell
AT
.
Next-generation psychiatric assessment: using smartphone sensors to monitor behavior and mental health
.
Psychiatr Rehabil J
.
2015
Sep
;
38
(
3
):
218
26
.
[PubMed]
1095-158X
7.
Burke
M
,
Marlow
C
,
Lento
T
: Social network activity and social well-being2010, pp 1909–1912.
8.
Canzian
L
,
Musolesi
M
. Trajectories of depression [Internet].
2015
ACM Int Jt Conf:1293–1304 TS–CrossRef.
9.
Cohen
S
,
Janicki-Deverts
D
,
Miller
GE
.
Psychological stress and disease
.
JAMA
.
2007
Oct
;
298
(
14
):
1685
7
.
[PubMed]
0098-7484
10.
Cummins
N
,
Joshi
J
,
Dhall
A
,
Sethu
V
,
Goecke
R
,
Epps
J
. Diagnosis of depression by behavioural signals [Internet]; in : Proceedings of the 3rd ACM international workshop on Audio/visual emotion challenge - AVEC ’13. New York, New York, USA, ACM Press,
2013
, pp 11–20.
11.
Cummins
N
,
Vlasenko
B
,
Sagha
H
,
Schuller
B
. Enhancing speech-based depression detection through gender dependent vowel-level formant features. In:
ten Teije
A
,
Popow
C
,
Holmes
JH
,
Sacchi
L
, editors
.
Artificial Intelligence in Medicine
.
Cham
:
Springer International Publishing
;
2017
. pp.
209
14
.
12.
David
ME
,
Roberts
JA
,
Christenson
B
: Too Much of a Good Thing: Investigating the Association between Actual Smartphone Use and Individual Well-Being,
2018
. DOI: .
13.
de Boer
SF
,
Buwalda
B
,
Koolhaas
JM
.
Untangling the neurobiology of coping styles in rodents: towards neural mechanisms underlying individual differences in disease susceptibility
.
Neurosci Biobehav Rev
.
2017
Mar
;
74
Pt B
:
401
22
.
[PubMed]
0149-7634
14.
Domhardt
M
,
Steubl
L
,
Baumeister
H
: Internet- and Mobile-Based Interventions for Mental and Somatic Conditions in Children and Adolescents A Systematic Review of Meta-analyses
2018
;1–14.
15.
Donkin
L
,
Glozier
N
.
Motivators and motivations to persist with online psychological interventions: a qualitative study of treatment completers
.
J Med Internet Res
.
2012
Jun
;
14
(
3
):
e91
.
[PubMed]
1438-8871
16.
Ebert
DD
,
Cuijpers
P
,
Muñoz
RF
,
Baumeister
H
.
Prevention of Mental Health Disorders using Internet and mobile-based Interventions: a narrative review and recommendations for future research
.
Front Psychiatry
.
2017
Aug
;
8
:
116
.
[PubMed]
1664-0640
17.
Ebert
D
,
Daele
T
,
Nordgreen
T
,
Karekla
M
,
Compare
TA
,
Zarbo
C
, et al
.
Internet and mobile-based psychological interventions: applications, efficacy and potential for improving mental health. A report of the EFPA e-health taskforce
.
Eur Psychol
.
2018
;
23
(
2
):
167
87
. 1016-9040
18.
Elhai
JD
,
Tiamiyu
MF
,
Weeks
JW
,
Levine
JC
,
Picard
KJ
,
Hall
BJ
.
Depression and emotion regulation predict objective smartphone use measured over one week
.
Pers Individ Dif
.
2017
;
•••
: 0191-8869
19.
Enders
CK
.
Using the expectation maximization algorithm to estimate coefficient alpha for scales with item-level missing data
.
Psychol Methods
.
2003
Sep
;
8
(
3
):
322
37
.
[PubMed]
1082-989X
20.
Ferdous
R
,
Osmani
V
,
Mayora
O
.
Smartphone app usage as a predictor of perceived stress levels at workplace
.
Int Conf Pervasive Comput Technol Healthc
.
2015
;
•••
: 2153-1633
21.
Goldstein
H
.
Hierarchical Data Modeling in the Social Sciences
.
J Educ Behav Stat
.
2008
;
•••
: 1076-9986
22.
Götz
FM
,
Stieger
S
,
Reips
UD
.
Users of the main smartphone operating systems (iOS, Android) differ only little in personality
.
PLoS One
.
2017
May
;
12
(
5
):
e0176921
.
[PubMed]
1932-6203
23.
Harari
GM
,
Lane
ND
,
Wang
R
,
Crosier
BS
,
Campbell
AT
,
Gosling
SD
.
Using Smartphones to Collect Behavioral Data in Psychological Science: Opportunities, Practical Considerations, and Challenges
.
Perspect Psychol Sci
.
2016
Nov
;
11
(
6
):
838
54
.
[PubMed]
1745-6916
24.
Harari
GM
,
Müller
SR
,
Aung
MS
,
Rentfrow
PJ
.
Smartphone sensing methods for studying behavior in everyday life
.
Curr Opin Behav Sci
.
2017
;
18
:
83
90
. 2352-1546
25.
ICD-11 - Mortality and Morbidity Statistics [cited 2019 Mar 13];Available from: https://icd.who.int/browse11/l-m/en
26.
Insel
TR
.
Digital Phenotyping
.
JAMA
.
2017
Oct
;
318
(
13
):
1215
6
.
[PubMed]
0098-7484
27.
Kampling
H
,
Baumeister
H
,
Jäckel
WH
,
Mittag
O
.
Prevention of depression in chronically physically ill adults
.
Cochrane Database Syst Rev
.
2014
; 1469-493X
28.
Kessler
RC
,
Berglund
P
,
Demler
O
,
Jin
R
,
Koretz
D
,
Merikangas
KR
, et al;
National Comorbidity Survey Replication
.
The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R)
.
JAMA
.
2003
Jun
;
289
(
23
):
3095
105
.
[PubMed]
0098-7484
29.
Lachmann
B
,
Sindermann
C
,
Sariyska
RY
,
Luo
R
,
Melchers
MC
,
Becker
B
, et al
.
The role of empathy and life satisfaction in internet and smartphone use disorder
.
Front Psychol
.
2018
Mar
;
9
:
398
.
[PubMed]
1664-1078
30.
Längkvist
M
,
Karlsson
L
,
Loutfi
A
.
A review of unsupervised feature learning and deep learning for time-series modeling
.
Pattern Recognit Lett
.
2014
;
42
:
11
24
. 0167-8655
31.
Laurenceau
J-P
,
Bolger
N
:
Analyzing Diary and Intensive Longitudinal Data from Dyads.
32.
LiKamWa R
. Liu Y, Lane ND, Zhong L: MoodScope [Internet]; in : Proceeding of the 11th annual international conference on Mobile systems, applications, and services - MobiSys ’13. New York, New York, USA, ACM Press,
2013
, p 389.
33.
MacCallum
RC
,
Kim
C
,
Malarkey
WB
,
Kiecolt-Glaser
JK
.
Studying multivariate change using multilevel models and latent curve models
.
Multivariate Behav Res
.
1997
Jul
;
32
(
3
):
215
53
.
[PubMed]
0027-3171
34.
Markowetz
A
,
Błaszkiewicz
K
,
Montag
C
,
Switala
C
,
Schlaepfer
TE
: Psycho-Informatics Big Data shaping modern psychometics.pdf
2014
.
35.
McCord
B
,
Rodebaugh
TL
,
Levinson
CA
.
Facebook: social uses and anxiety
.
Comput Human Behav
.
2014
;
34
:
23
7
. 0747-5632
36.
Mehrotra
A
,
Musolesi
M
.
Designing Effective Movement Digital Biomarkers for Unobtrusive Emotional State Mobile Monitoring
; in : Proceedings of the 1st Workshop on Digital Biomarkers - DigitalBiomarkers ’17.
2017
a. DOI:
37.
Mehrotra
A
,
Pejovic
V
,
Musolesi
M
: SenSocial2014. DOI: .
38.
Milczarek
M
,
Schneider
E
,
Gonzalez
ER
.
OSH in figures: stress at work — facts and figures
.
Luxembourg
:
Office for Official Publications of the European Communities
;
2009
.
39.
Miller
G
.
The Smartphone Psychology Manifesto
.
Perspect Psychol Sci
.
2012
May
;
7
(
3
):
221
37
.
[PubMed]
1745-6916
40.
Miotto
R
,
Wang
F
,
Wang
S
,
Jiang
X
,
Dudley
JT
.
Deep learning for healthcare: review, opportunities and challenges
.
Brief Bioinform
.
2018
Nov
;
19
(
6
):
1236
46
.
[PubMed]
1467-5463
41.
Mohr
DC
,
Tomasino
KN
,
Lattie
EG
,
Palac
HL
,
Kwasny
MJ
,
Weingardt
K
, et al
.
Intellicare: an eclectic, skills-based app suite for the treatment of depression and anxiety
.
J Med Internet Res
.
2017
a
Jan
;
19
(
1
):
e10
.
[PubMed]
1438-8871
42.
Mohr
DC
,
Zhang
M
,
Schueller
SM
.
CP13CH02-Mohr ARI 4 April 2017 15:40 Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning
.
Annu Rev Clin Psychol
.
2017
b;
13
:
23
47
.
[PubMed]
1548-5943
43.
Montag
C
,
Baumeister
H
,
Kannen
C
,
Sariyska
R
,
Messner
EM
,
Brand
M
.
Concept, Possibilities and Pilot-Testing of a New Smartphone Application for the Social and Life Sciences to Study Human Behavior Including Validation Data from Personality Psychology
.
J Multidisciplinary Scientific Journal
.
2019
;
2
(
2
):
102
15
.
44.
Montag
C
,
Błaszkiewicz
K
,
Sariyska
R
,
Lachmann
B
,
Andone
I
,
Trendafilov
B
, et al
.
Smartphone usage in the 21st century: who is active on WhatsApp
.
BMC Res Notes
.
2015
Aug
;
8
(
1
):
331
.
[PubMed]
1756-0500
45.
Montag
C
,
Diefenbach
S
.
Towards Homo Digitalis: Important Research Issues for Psychology and the Neurosciences at the Dawn of the Internet of Things and the Digital Society
.
Sustainability
.
2018
;
10
(
2
):
415
. 1548-7733
46.
Montag
C
,
Duke
É
,
Markowetz
A
.
Toward Psychoinformatics: Computer Science Meets Psychology
.
Comput Math Methods Med
.
2016
;
2016
:
2983685
.
[PubMed]
1748-670X
47.
Moussavi
S
,
Chatterji
S
,
Verdes
E
,
Tandon
A
,
Patel
V
,
Ustun
B
.
Depression, chronic diseases, and decrements in health: results from the World Health Surveys
.
Lancet
.
2007
Sep
;
370
(
9590
):
851
8
.
[PubMed]
0140-6736
48.
Mund
M
,
Mitte
K
.
The costs of repression: a meta-analysis on the relation between repressive coping and somatic diseases
.
Health Psychol
.
2012
Sep
;
31
(
5
):
640
9
.
[PubMed]
0278-6133
49.
Nezlek
JB
,
Schröder-Abé
M
,
Schütz
A
.
Mehrebenenanalysen in der psychologischen Forschung
.
Psychol Rundsch
.
2006
;
57
(
4
):
213
23
. 0033-3042
50.
Nezlek
JB
. Multilevel modeling for psychologists.; in : APA handbook of research methods in psychology, Vol 3: Data analysis and research publication.
2012
. DOI:
51.
Nezlek
JB
.
Multilevel Random Coefficient Analyses of Event- and Interval-Contingent Data in Social and Personality Psychology Research
.
Pers Soc Psychol Bull
.
2001
;
27
(
7
):
771
85
. 0146-1672
52.
Onnela
JP
,
Rauch
SL
.
Harnessing Smartphone-Based Digital Phenotyping to Enhance Behavioral and Mental Health
.
Neuropsychopharmacology
.
2016
Jun
;
41
(
7
):
1691
6
.
[PubMed]
0893-133X
53.
Oquendo
MA
,
Baca-Garcia
E
,
Artés-Rodríguez
A
,
Perez-Cruz
F
,
Galfalvy
HC
,
Blasco-Fontecilla
H
, et al
.
Machine learning and data mining: strategies for hypothesis generation
.
Mol Psychiatry
.
2012
Oct
;
17
(
10
):
956
9
.
[PubMed]
1359-4184
54.
Paulhus
DL
. Socially Desirable Responding on Self-Reports; in : Encyclopedia of Personality and Individual Differences.
2017
. DOI:
55.
Primack
BA
,
Shensa
A
,
Escobar-Viera
CG
,
Barrett
EL
,
Sidani
JE
,
Colditz
JB
, et al
.
Use of multiple social media platforms and symptoms of depression and anxiety: A nationally-representative study among U.S. young adults
.
Comput Human Behav
.
2017
;
69
:
1
9
. 0747-5632
56.
Przybylski
AK
,
Weinstein
N
.
A Large-Scale Test of the Goldilocks Hypothesis : Quantifying the Relations Between Digital-Screen Use and the Mental Well-Being of Adolescents
.
Assoc Psychol Sci
.
2017
;
28
(
2
):
204
15
.
57.
Raballo
A
.
Digital phenotyping: an overarching framework to capture our extended mental states
.
Lancet Psychiatry
.
2018
Mar
;
5
(
3
):
194
5
.
[PubMed]
2215-0366
58.
Rathner
EM
,
Djamali
J
,
Terhorst
Y
,
Schuller
B
,
Cummins
N
,
Salamon
G
, et al
How did you like 2017? Detection of language markers of depression and narcissism in personal narratives
.
Interspeech
;
2018
a. pp.
3388
92
.
59.
Rathner
E-M
,
Terhorst
Y
,
Cummins
N
,
Schuller
B
,
Baumeister
H
: State of mind: Classification through self-reported affect and word use in speech. Interspeech 2018
2018
b;267–271.
60.
Rathner
EM
,
Probst
T
.
Mobile Applikationen in der psychotherapeutischen Praxis: chancen und Risiken
.
Psychother Dialog
.
2018
;
4
(
19
):
51
5
. 1438-7026
61.
Richards
D
,
Sanabria
AS
.
Point-prevalence of depression and associated risk factors
.
J Psychol
.
2014
May-Jun
;
148
(
3
):
305
26
.
[PubMed]
0022-3980
62.
Richards
D
.
Prevalence and clinical course of depression: a review
.
Clin Psychol Rev
.
2011
Nov
;
31
(
7
):
1117
25
.
[PubMed]
0272-7358
63.
Rotondi
V
,
Stanca
L
,
Tomasuolo
M
.
Connecting alone: smartphone use, quality of social interactions and well-being
.
J Econ Psychol
.
2017
;
63
:
17
26
. 0167-4870
64.
Rozgonjuk
D
,
Levine
JC
,
Hall
BJ
,
Elhai
JD
.
The association between problematic smartphone use, depression and anxiety symptom severity, and objectively measured smartphone use over one week
.
Comput Human Behav
.
2018
;
87
:
10
7
. 0747-5632
65.
Rubeis
G
,
Steger
F
.
Internet- und mobilgestützte Interventionen bei psychischen Störungen : implementierung in Deutschland aus ethischer Sicht
.
Nervenarzt
.
2019
May
;
90
(
5
):
497
502
.
[PubMed]
0028-2804
66.
Runyan
JD
,
Steenbergh
TA
,
Bainbridge
C
,
Daugherty
DA
,
Oke
L
,
Fry
BN
.
A smartphone ecological momentary assessment/intervention “app” for collecting real-time data and promoting self-awareness
.
PLoS One
.
2013
Aug
;
8
(
8
):
e71325
.
[PubMed]
1932-6203
67.
Russell
JA
,
Barrett
LF
.
Core affect, prototypical emotional episodes, and other things called emotion: dissecting the elephant
.
J Pers Soc Psychol
.
1999
May
;
76
(
5
):
805
19
.
[PubMed]
0022-3514
68.
Russell
JA
.
Core affect and the psychological construction of emotion
.
Psychol Rev
.
2003
Jan
;
110
(
1
):
145
72
.
[PubMed]
0033-295X
69.
Saeb
S
,
Zhang
M
,
Karr
CJ
,
Schueller
SM
,
Corden
ME
,
Kording
KP
, et al
.
Mobile phone sensor correlates of depressive symptom severity in daily-life behavior: an exploratory study
.
J Med Internet Res
.
2015
Jul
;
17
(
7
):
e175
.
[PubMed]
1438-8871
70.
Sano
A
,
Picard
RW
.
Stress Recognition Using Wearable Sensors and Mobile Phones [Internet
]; in :
2013 Humaine Association Conference on Affective Computing and Intelligent Interaction
.
IEEE
,
2013
, pp
671
676
.
71.
Sariyska
R
,
Rathner
EM
,
Baumeister
H
,
Montag
C
.
Feasibility of Linking Molecular Genetic Markers to Real-World Social Network Size Tracked on Smartphones
.
Front Neurosci
.
2018
Dec
;
12
:
945
.
[PubMed]
1662-4548
72.
Scherr
S
.
Traditional media use and depression in the general population: evidence for a non-linear relationship
.
Curr Psychol
.
2018
;
•••
: 1046-1310
73.
Schwartz
HA
,
Eichstaedt
J
,
Kern
ML
,
Park
G
,
Sap
M
,
Stillwell
D
, et al
Towards Assessing Changes in Degree of Depression through Facebook
[
Internet
]
Proc Work Comput Linguist Clin Psychol From Linguist Signal to Clin Real
;
2014
. pp.
118
25
.
74.
Seabrook
EM
,
Kern
ML
,
Rickard
NS
.
Social Networking Sites, Depression, and Anxiety: A Systematic Review
.
JMIR Ment Health
.
2016
Nov
;
3
(
4
):
e50
.
[PubMed]
2368-7959
75.
Servia-Rodríguez
S
,
Rachuri
KK
,
Mascolo
C
,
Rentfrow
PJ
,
Lathia
N
,
Sandstrom
GM
.
Mobile
Sensing at the Service of Mental Well-being [Internet]; in :
International Conference on World Wide Web
.
New York, New York, USA
,
ACM Press
,
2017
a, pp
103
112
.
76.
Shiffman
S
.
Ecological momentary assessment (EMA) in studies of substance use
.
Psychol Assess
.
2009
Dec
;
21
(
4
):
486
97
.
[PubMed]
1040-3590
77.
Shilton
K
,
Sayles
S
.
We aren’t all going to be on the same page about ethics: ethical practices and challenges in research on digital and social media
.
Proc Annu Hawaii Int Conf Syst Sci
.
2016
;2016–March:
1909
1918
.
78.
Shilton
K
.
Four Billion Little Brothers? Privacy, mobile phones, and ubiquitous data collection
.
Cent Embed Netw Sens
.
2009
;
7
:
1
7
.
79.
Slavich
GM
.
Life Stress and Health: A Review of Conceptual Issues and Recent Findings
.
Teach Psychol
.
2016
Oct
;
43
(
4
):
346
55
.
[PubMed]
0098-6283
80.
Stasak
B
,
Epps
J
,
Cummins
N
,
Goecke
R
,
Eng
E
,
South
N
.
An Investigation of Emotional Speech in Depression Classification National Information Communications Technology (NICTA) Human-Centred Technology
.
Canberra, Australia
:
University of Canberra
;
2016
. pp.
485
9
.
81.
Stone
AA
,
Shiffman
S
: Capturing Momentary, Self-Report Data: A Proposal for Reporting Guidelines,
2002
.
82.
Suhara
Y
,
Xu
Y
,
Pentland
A
. “Sandy”: DeepMood [Internet]; in : Proceedings of the 26th International Conference on World Wide Web - WWW ’17. New York, New York, USA, ACM Press,
2017
, pp 715–724.
83.
Tandoc
EC
Jr
,
Ferrucci
P
,
Duffy
M
.
Facebook use, envy, and depression among college students: is facebooking depressing
.
Comput Human Behav
.
2015
;
43
:
139
46
. 0747-5632
84.
Thomée
S
.
Mobile Phone Use and Mental Health. A Review of the Research That Takes a Psychological Perspective on Exposure
.
Int J Environ Res Public Health
.
2018
Nov
;
15
(
12
):
2692
.
[PubMed]
1661-7827
85.
Torous
J
,
Onnela
JP
,
Keshavan
M
.
New dimensions and new tools to realize the potential of RDoC: digital phenotyping via smartphones and connected devices
.
Transl Psychiatry
.
2017
Mar
;
7
(
3
):
e1053
.
[PubMed]
2158-3188
86.
van Praag
HM
.
Can stress cause depression
.
Prog Neuropsychopharmacol Biol Psychiatry
.
2004
Aug
;
28
(
5
):
891
907
.
[PubMed]
0278-5846
87.
Verduyn
P
,
Ybarra
O
,
Résibois
M
,
Jonides
J
,
Kross
E
.
Do Social Network Sites Enhance or Undermine Subjective Well-Being? A Critical Review
.
Soc Issues Policy Rev
.
2017
;
11
(
1
):
274
302
. 1751-2395
88.
Vildjiounaite
E
,
Kallio
J
,
Kyllönen
V
,
Nieminen
M
,
Määttänen
I
,
Lindholm
M
, et al
.
Unobtrusive stress detection on the basis of smartphone usage data
.
Pers Ubiquitous Comput
.
2018
;
22
(
4
):
671
88
. 1617-4909
89.
Wang
J
.
Work stress as a risk factor for major depressive episode(s)
.
Psychol Med
.
2005
Jun
;
35
(
6
):
865
71
.
[PubMed]
0033-2917
90.
World Health Organization
. International statistical classification of diseases and related health problems (11th Revision).
2018
. Retrieved from https://icd.who.int/browse11/l-m/en
92.
Zillmann
D
;
ZILLMANN D
.
Mood Management Through Communication Choices
.
Am Behav Sci
.
1988
b;
31
(
3
):
327
40
. 0002-7642
You do not currently have access to this content.