New Frontiers in Ambulatory Assessment

With the increasing use of smartphone technologies and wearable biosensors, we are currently undergoing what many have termed a “data revolution,” where intensive, multichannel data are passively collected over long time frames. Such procedures are transforming the way psychologists conceptualize research and have the potential to spur important advances in the study of close relationships. This proof-of-concept study from the Couple Mobile Sensing Project, a partnership between psychologists and engineers, combines big data and ambulatory assessment methodologies to study multimodal, microprocesses in couples’ everyday lives. These data collection procedures are designed to test how characteristics of everyday behavioral, physiological, and vocal interactions are integrated within and across individuals. We present two mini-illustrations to show how these data can be synchronized across modalities and partners and can be linked to generalized relationship dimensions. Discussion highlights the potential and challenges of capturing multimodal, multiperson, real-time, naturally occurring social phenomena.

[1]  D. Bond,et al.  Behavioral response to a just-in-time adaptive intervention (JITAI) to reduce sedentary behavior in obese adults: Implications for JITAI optimization. , 2015, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[2]  C. Hazan,et al.  Coregulation, Dysregulation, Self-Regulation: An Integrative Analysis and Empirical Agenda for Understanding Adult Attachment, Separation, Loss, and Recovery , 2008, Personality and social psychology review : an official journal of the Society for Personality and Social Psychology, Inc.

[3]  J. Holt-Lunstad,et al.  Influence of a “Warm Touch” Support Enhancement Intervention Among Married Couples on Ambulatory Blood Pressure, Oxytocin, Alpha Amylase, and Cortisol , 2008, Psychosomatic medicine.

[4]  Mika P. Tarvainen,et al.  Kubios HRV - Heart rate variability analysis software , 2014, Comput. Methods Programs Biomed..

[5]  Julian F. Thayer,et al.  The Non-invasive Assessment of Autonomic Influences on the Heart Using Impedance Cardiography and Heart Rate Variability , 2010 .

[6]  Kelly A. Brennan,et al.  An item response theory analysis of self-report measures of adult attachment. , 2000, Journal of personality and social psychology.

[7]  Matthias R. Mehl,et al.  An Empirical Analysis of the Obtrusiveness of and Participants' Compliance with the Electronically Activated Recorder (EAR) , 2007 .

[8]  Carmen Vidaurre,et al.  BioSig: The Free and Open Source Software Library for Biomedical Signal Processing , 2011, Comput. Intell. Neurosci..

[9]  Rosalind W. Picard,et al.  A Wearable Sensor for Unobtrusive, Long-Term Assessment of Electrodermal Activity , 2010, IEEE Transactions on Biomedical Engineering.

[10]  F. Betz Handbook of Research Methods , 2011 .

[11]  Mustapha Mezghanni,et al.  Real-time tracking of neighborhood surroundings and mood in urban drug misusers: application of a new method to study behavior in its geographical context. , 2014, Drug and alcohol dependence.

[12]  Sivanesan Dakshanamurthy,et al.  Big data: the next frontier for innovation in therapeutics and healthcare , 2014, Expert review of clinical pharmacology.

[13]  Timothy W. Smith,et al.  Evaluative threat and ambulatory blood pressure: cardiovascular effects of social stress in daily experience. , 2012, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[14]  Lauren M. Papp,et al.  Spouses' cortisol associations and moderators: testing physiological synchrony and connectedness in everyday life. , 2013, Family process.

[15]  J. Coan,et al.  Social Baseline Theory: The Role of Social Proximity in Emotion and Economy of Action , 2011 .

[16]  M. Mehl,et al.  Handbook of research methods for studying daily life , 2012 .

[17]  Martin E. P. Seligman,et al.  The Online Social Self , 2014, Assessment.

[18]  R. Repetti,et al.  For Better or Worse? Coregulation of Couples' Cortisol Levels and Mood States Defining Coregulation Cohabiting Partners May Influence Each Other's Moods and Physiology; for Example, Mcclintock (1971) Has Reported That Roommates' Men- Strual Cycles Become Synchronized over Time. Other Researchers , 2022 .

[19]  Murtaza Haider,et al.  Beyond the hype: Big data concepts, methods, and analytics , 2015, Int. J. Inf. Manag..

[20]  S. Porges,et al.  Vagal tone and the physiological regulation of emotion. , 1994, Monographs of the Society for Research in Child Development.

[21]  J. Pennebaker,et al.  The Electronically Activated Recorder (EAR): A device for sampling naturalistic daily activities and conversations , 2001, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[22]  N. Bolger,et al.  Using diary methods to study marital and family processes. , 2005, Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association.

[23]  B. Hasler,et al.  Couples' Nighttime Sleep Efficiency and Concordance: Evidence for Bidirectional Associations With Daytime Relationship Functioning , 2010, Psychosomatic medicine.

[24]  Matthew S. Goodwin,et al.  Telemetric monitoring in the behavior sciences , 2008, Behavior research methods.

[25]  G. Margolin,et al.  Analysis of the association between marital relationships and health problems: an interactional perspective. , 1992, Psychological bulletin.

[26]  M. Dawson,et al.  The electrodermal system , 2007 .

[27]  J. Russell,et al.  Facial and vocal expressions of emotion. , 2003, Annual review of psychology.

[28]  M. Hallett The Physiology of Will , 2015, Journal of the Neurological Sciences.

[29]  M. Hofer,et al.  Relationships as regulators: a psychobiologic perspective on bereavement. , 1984, Psychosomatic medicine.

[30]  W. Chaplin,et al.  Self-Esteem and the Acute Effect of Anxiety on Ambulatory Blood Pressure , 2015, Psychosomatic medicine.

[31]  J. Kiecolt-Glaser,et al.  The physiology of marriage: pathways to health , 2003, Physiology & Behavior.

[32]  Adela C. Timmons,et al.  Physiological linkage in couples and its implications for individual and interpersonal functioning: A literature review. , 2015, Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association.

[33]  M. Mehl,et al.  Naturalistically observed swearing, emotional support, and depressive symptoms in women coping with illness. , 2011, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[34]  J. Mathieu,et al.  Understanding and estimating the power to detect cross-level interaction effects in multilevel modeling. , 2012, The Journal of applied psychology.

[35]  J. Fleiss,et al.  Intraclass correlations: uses in assessing rater reliability. , 1979, Psychological bulletin.

[36]  Gayla Margolin,et al.  Correlates and characteristics of adolescents' encoded emotional arousal during family conflict. , 2012, Emotion.

[37]  Michael Potegal,et al.  Screaming, yelling, whining, and crying: categorical and intensity differences in vocal expressions of anger and sadness in children's tantrums. , 2011, Emotion.

[38]  R. Mccall,et al.  The Genetic and Environmental Origins of Learning Abilities and Disabilities in the Early School , 2007, Monographs of the Society for Research in Child Development.

[39]  M. Benedek,et al.  Decomposition of skin conductance data by means of nonnegative deconvolution , 2010, Psychophysiology.

[40]  M. Mehl,et al.  Cancer conversations in context: naturalistic observation of couples coping with breast cancer. , 2014, Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association.

[41]  S. Shiffman,et al.  Ecological momentary assessment. , 2008, Annual review of clinical psychology.

[42]  J. Manyika Big data: The next frontier for innovation, competition, and productivity , 2011 .