Co-variation of depressive mood and locomotor dynamics evaluated by ecological momentary assessment in healthy humans

Jinhyuk Kim, Toru Nakamura, Hiroe Kikuchi, Tsukasa Sasaki, Yoshiharu Yamamoto, Jinhyuk Kim, Toru Nakamura, Hiroe Kikuchi, Tsukasa Sasaki, Yoshiharu Yamamoto

Abstract

Computerized ecological momentary assessment (EMA) is widely accepted as a "gold standard" method for capturing momentary symptoms repeatedly experienced in daily life. Although many studies have addressed the within-individual temporal variations in momentary symptoms compared with simultaneously measured external criteria, their concurrent associations, specifically with continuous physiological measures, have not been rigorously examined. Therefore, in the present study, we first examined the variations in momentary symptoms by validating the associations among self-reported symptoms measured simultaneously (depressive mood, anxious mood, and fatigue) and then investigated covariant properties between the symptoms (especially, depressive mood) and local statistics of locomotor activity as the external objective criteria obtained continuously. Healthy subjects (N = 85) from three different populations (adolescents, undergraduates, and office workers) wore a watch-type computer device equipped with EMA software for recording the momentary symptoms experienced by the subjects. Locomotor activity data were also continuously obtained by using an actigraph built into the device. Multilevel modeling analysis confirmed convergent associations by showing positive correlations among momentary symptoms. The increased intermittency of locomotor activity, characterized by a combination of reduced activity with occasional bursts, appeared concurrently with the worsening of depressive mood. Further, this association remained statistically unchanged across groups regardless of group differences in age, lifestyle, and occupation. These results indicate that the temporal variations in the momentary symptoms are not random but reflect the underlying changes in psychophysiological variables in daily life. In addition, our findings on the concurrent changes in depressive mood and locomotor activity may contribute to the continuous estimation of changes in depressive mood and early detection of depressive disorders.

Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1. Dependency of univariate model coefficients…
Figure 1. Dependency of univariate model coefficients for depressive mood on different time frames.
The estimated values of the univariate model coefficient for depressive mood scores are shown in a colored matrix form consisting of 25 rows (different locations) and 12 columns (different sizes). Each grid cell indicates some specific location and size of a time frame used for calculating the local statistics of locomotor activity. A color in each cell represents the value of the model coefficient (γ10) of the predictors: (a) Mean, (b) Skewness, and (c) Detrended skewness. False discovery rate with a q value of 0.05 was used as the multiple comparison adjustment. Only significant cases are shown by colors. Color bars indicate the value of the model coefficient. Note that the univariate model used for the analysis is as follows; Depressive mood scoreij = γ00 + γ10 (Local statistics of locomotor activityij) + ζ0i + εij (see Results for details).
Figure 2. Dependency of best-fitting model coefficients…
Figure 2. Dependency of best-fitting model coefficients for depressive mood on different time frames.
The estimate of the model coefficient of the predictor: (a) Mean (γ10) and (c) Detrended skewness (γ20) of the best-fitting model for depressive mood scores (Depressive mood scoresij = γ00 + γ10 (Meanij) + γ20 (Detrended skewnessij) + ζ0i + ζ1i (Meanij) + εij) as a function of the location. In panels (a) and (c), the size of the time frame was fixed at 60 min. The same is shown in panels (b) and (d), except for the dependency on the size of the time frame. For evaluation, the location of the time frame was fixed at 0 min; thus, the frame was centered around the EMA recordings. Error bars indicate standard error of the coefficients. The asterisk and dagger indicate significant cases at the 0.05 and 0.10, respectively.
Figure 3. Fluctuation of depressive mood and…
Figure 3. Fluctuation of depressive mood and locomotor activity in an office worker.
Filled circles indicate depressive mood scores (y-axis on the left side) recorded by ecological momentary assessment (EMA) over seven consecutive days. The inset in the upper left represents the watch-type activity monitor equipped with EMA software. The locomotor activity measured simultaneously (zero-crossing counts within every 1 min time interval) is also shown (y-axis on the right side). The periods shaded in gray are times during which the subject was sleeping or had taken off the device.

References

    1. Stone AA, Shiffman S (1994) Ecological momentary assessment (EMA) in behavioral medicine. Ann Behav Med 16: 199-202.
    1. Shiffman S, Stone AA, Hufford MR (2008) Ecological momentary assessment. Annu Rev Clin Psychol 4: 1-32. doi:. PubMed: .
    1. Stone AA, Shiffman S, Schwartz JE, Broderick JE, Hufford MR (2002) Patient non-compliance with paper diaries. BMJ 324: 1193-1194. doi:. PubMed: .
    1. Conner TS, Barrett LF (2012) Trends in ambulatory self-report: the role of momentary experience in psychosomatic medicine. Psychosom Med 74: 327-337. doi:. PubMed: .
    1. Kumar S, Nilsen W, Pavel M, Srivastava M (2013) Mobile health: revolutionizing healthcare through transdisciplinary research. IEEE Comput 46: 28-35. doi:.
    1. American Psychiatric Association (2000) Diagnostic and statistical manual of mental disorders. Washington, D.C.: American Psychiatric Press.
    1. Thewissen V, Bentall RP, Lecomte T, van Os J, Myin-Germeys I (2008) Fluctuations in self-esteem and paranoia in the context of daily life. J Abnorm Psychol 117: 143-153. doi:. PubMed: .
    1. Steptoe A, Leigh ES, Kumari M (2011) Positive affect and distressed affect over the day in older people. Psychol Aging 26: 956-965. doi:. PubMed: .
    1. Kimhy D, Delespaul P, Corcoran C, Ahn H, Yale S et al. (2006) Computerized experience sampling method (ESMc): assessing feasibility and validity among individuals with schizophrenia. J Psychiatr Res 40: 221-230. doi:. PubMed: .
    1. Kamarck TW, Schwartz JE, Shiffman S, Muldoon MF, Sutton-Tyrrell K et al. (2005) Psychosocial stress and cardiovascular risk: what is the role of daily experience? J Pers 73: 1749-1774. doi:. PubMed: .
    1. Kamarck TW, Shiffman SM, Smithline L, Goodie JL, Paty JA et al. (1998) Effects of task strain, social conflict, and emotional activation on ambulatory cardiovascular activity: daily life consequences of recurring stress in a multiethnic adult sample. Health Psychol 17: 17-29. doi:. PubMed: .
    1. Smith TW, Birmingham W, Uchino BN (2012) Evaluative threat and ambulatory blood pressure: cardiovascular effects of social stress in daily experience. Health Psychol 31: 763-766. doi:. PubMed: .
    1. Robles TF, Shetty V, Zigler CM, Glover DA, Elashoff D et al. (2011) The feasibility of ambulatory biosensor measurement of salivary alpha amylase: relationships with self-reported and naturalistic psychological stress. Biol Psychol 86: 50-56. doi:. PubMed: .
    1. van Eck M, Berkhof H, Nicolson N, Sulon J (1996) The effects of perceived stress, traits, mood states, and stressful daily events on salivary cortisol. Psychosom Med 58: 447-458. PubMed: .
    1. Smyth J, Ockenfels MC, Porter L, Kirschbaum C, Hellhammer DH et al. (1998) Stressors and mood measured on a momentary basis are associated with salivary cortisol secretion. Psychoneuroendocrinology 23: 353-370. doi:. PubMed: .
    1. Steptoe A, Gibson EL, Hamer M, Wardle J (2007) Neuroendocrine and cardiovascular correlates of positive affect measured by ecological momentary assessment and by questionnaire. Psychoneuroendocrinology 32: 56-64. doi:. PubMed: .
    1. Ritz T, Rosenfield D, Dewilde S, Steptoe A (2010) Daily mood, shortness of breath, and lung function in asthma: concurrent and prospective associations. J Psychosom Res 69: 341-351. doi:. PubMed: .
    1. Chandra S, Scharf D, Shiffman S (2011) Within-day temporal patterns of smoking, withdrawal symptoms, and craving. Drug Alcohol Depend 117: 118-125. doi:. PubMed: .
    1. Shiffman S, Balabanis MH, Gwaltney CJ, Paty JA, Gnys M et al. (2007) Prediction of lapse from associations between smoking and situational antecedents assessed by ecological momentary assessment. Drug Alcohol Depend 91: 159-168. doi:. PubMed: .
    1. Muraven M, Collins RL, Shiffman S, Paty JA (2005) Daily fluctuations in self-control demands and alcohol intake. Psychol Addict Behav 19: 140-147. doi:. PubMed: .
    1. Jahng S, Solhan MB, Tomko RL, Wood PK, Piasecki TM et al. (2011) Affect and alcohol use: an ecological momentary assessment study of outpatients with borderline personality disorder. J Abnorm Psychol 120: 572-584. doi:. PubMed: .
    1. Dunton GF, Atienza AA, Castro CM, King AC (2009) Using ecological momentary assessment to examine antecedents and correlates of physical activity bouts in adults age 50+ years: a pilot study. Ann Behav Med 38: 249-255. doi:. PubMed: .
    1. Wichers M, Peeters F, Rutten BP, Jacobs N, Derom C et al. (2012) A time-lagged momentary assessment study on daily life physical activity and affect. Health Psychol 31: 135-144. doi:. PubMed: .
    1. Teicher MH (1995) Actigraphy and motion analysis: new tools for psychiatry. Harv Rev Psychiatry 3: 18-35. doi:. PubMed: .
    1. Teicher MH, Glod CA, Magnus E, Harper D, Benson G et al. (1997) Circadian rest-activity disturbances in seasonal affective disorder. Arch Gen Psychiatry 54: 124-130. doi:. PubMed: .
    1. Burton C, McKinstry B, Szentagotai Tătar A, Serrano-Blanco A, Pagliari C et al. (2013) Activity monitoring in patients with depression: a systematic review. J Affect Disord 145: 21-28. doi:. PubMed: .
    1. Indic P, Murray G, Maggini C, Amore M, Meschi T, et al. (2012) Multi-scale motility amplitude associated with suicidal thoughts in major depression. PLOS ONE 7: e38761. doi:. PubMed: . doi: PubMed:
    1. Walther S, Hügli S, Höfle O, Federspiel A, Horn H et al. (2012) Frontal white matter integrity is related to psychomotor retardation in major depression. Neurobiol Dis 47: 13-19. doi:. PubMed: .
    1. Volkers AC, Tulen JH, van den Broek WW, Bruijn JA, Passchier J et al. (2003) Motor activity and autonomic cardiac functioning in major depressive disorder. J Affect Disord 76: 23-30. doi:. PubMed: .
    1. Berle JO, Hauge ER, Oedegaard KJ, Holsten F, Fasmer OB (2010) Actigraphic registration of motor activity reveals a more structured behavioural pattern in schizophrenia than in major depression. BMC Res Notes 3: 149. doi:. PubMed: .
    1. Nakamura T, Kiyono K, Yoshiuchi K, Nakahara R, Struzik ZR et al. (2007) Universal scaling law in human behavioral organization. Phys Rev Lett 99: 138103. doi:. PubMed: .
    1. Nakamura T, Takumi T, Takano A, Aoyagi N, Yoshiuchi K et al. (2008) Of mice and men--universality and breakdown of behavioral organization. PLOS ONE 3: e2050. doi:. PubMed: .
    1. Kahneman D, Krueger AB, Schkade DA, Schwarz N, Stone AA (2004) A survey method for characterizing daily life experience: the day reconstruction method. Science 306: 1776-1780. doi:. PubMed: .
    1. McColl E (2004) Best practice in symptom assessment: a review. Gut 53: 49-54. PubMed: .
    1. Stone AA, Schwartz JE, Schkade D, Schwarz N, Krueger A et al. (2006) A population approach to the study of emotion: diurnal rhythms of a working day examined with the Day Reconstruction Method. Emotion 6: 139-149. doi:. PubMed: .
    1. Stone AA, Broderick JE, Porter LS, Kaell AT (1997) The experience of rheumatoid arthritis pain and fatigue: examining momentary reports and correlates over one week. Arthritis Care Res 10: 185-193. doi:. PubMed: .
    1. Stone AA, Broderick JE, Shiffman SS, Schwartz JE (2004) Understanding recall of weekly pain from a momentary assessment perspective: absolute agreement, between- and within-person consistency, and judged change in weekly pain. Pain 107: 61-69. doi:. PubMed: .
    1. Friedberg F, Sohl SJ (2008) Memory for fatigue in chronic fatigue syndrome: the relation between weekly recall and momentary ratings. Int J Behav Med 15: 29-33. doi:. PubMed: .
    1. Stavrakakis N, de Jonge P, Ormel J, Oldehinkel AJ (2012) Bidirectional prospective associations between physical activity and depressive symptoms. The TRAILS Study. J Adolesc Health Care 50: 503-508. doi:.
    1. Lindwall M, Larsman P, Hagger MS (2011) The reciprocal relationship between physical activity and depression in older European adults: a prospective cross-lagged panel design using SHARE data. Health Psychol 30: 453-462. doi:. PubMed: .
    1. Harris AH, Cronkite R, Moos R (2006) Physical activity, exercise coping, and depression in a 10-year cohort study of depressed patients. J Affect Disord 93: 79-85. doi:. PubMed: .
    1. Strawbridge WJ (2002) Physical activity reduces the risk of subsequent depression for older adults. Am J Epidemiol 156: 328-334. doi:. PubMed: .
    1. Camacho TC, Roberts RE, Lazarus NB, Kaplan GA, Cohen RD (1991) Physical activity and depression: evidence from the Alameda County Study. Am J Epidemiol 134: 220-231. PubMed: .
    1. Damasio AR (2004) Looking for Spinoza: joy, sorrow, and the feeling brain. London: Vintage Publishing House.
    1. Kim J, Kikuchi H, Yamamoto Y (2013) Systematic comparison between ecological momentary assessment and day reconstruction method for fatigue and mood states in healthy adults. Br J Health Psychol 18: 155-167. doi:. PubMed: .
    1. Fukui I (1997) The depression and anxiety mood scale (DAMS): scale development and validation. JPN J Behav Ther 23: 83-93.
    1. Kikuchi H, Yoshiuchi K, Ohashi K, Yamamoto Y, Akabayashi A (2007) Tension-type headache and physical activity: an actigraphic study. Cephalalgia 27: 1236-1243. doi:. PubMed: .
    1. Sano W, Nakamura T, Yoshiuchi K, Kitajima T, Tsuchiya A et al. (2012) Enhanced persistency of resting and active periods of locomotor activity in schizophrenia. PLOS ONE 7: e43539. doi:. PubMed: .
    1. Schwartz JE, Stone AA (1998) Strategies for analyzing ecological momentary assessment data. Health Psychol 17: 6-16. doi:. PubMed: .
    1. Benjamini Y, Yekutieli D (2001) The control of the false discovery rate in multiple testing under dependency. Ann Stat 29: 1165-1188. doi:.
    1. Suls J, Bunde J (2005) Anger, anxiety, and depression as risk factors for cardiovascular disease: the problems and implications of overlapping affective dispositions. Psychol Bull 131: 260-300. doi:. PubMed: .
    1. Bleil ME, Gianaros PJ, Jennings JR, Flory JD, Manuck SB (2008) Trait negative affect: toward an integrated model of understanding psychological risk for impairment in cardiac autonomic function. Psychosom Med 70: 328-337. doi:. PubMed: .
    1. Thieme K, Turk DC, Flor H (2004) Comorbid depression and anxiety in Fibromyalgia syndrome: Relationship to somatic and psychosocial variables. Psychosom Med 66: 837-844. doi:. PubMed: .
    1. Demyttenaere K, De Fruyt J, Stahl SM (2005) The many faces of fatigue in major depressive disorder. Int J Neuropsychopharmacol 8: 93-105. doi:. PubMed: .
    1. Kennedy SH (2008) Core symptoms of major depressive disorder: relevance to diagnosis and treatment. Dialogues Clin Neurosci 10: 271-277. PubMed: .
    1. Tylee A, Gastpar M, Lepine JP, Mendlewicz J (1999) DEpress II (Depression Research in European Society II): a patient survey of the symptoms, disability and current management of depression in the community DEPRES Steering Committee. Int Clin Psychopharmacol 14: 139-151.
    1. Baldwin DS, Papakostas GI (2006) Symptoms of fatigue and sleepiness in major depressive disorder. J Clin Psychiatry 67 Suppl 6: 9-15. doi:. PubMed: .
    1. Skapinakis P, Lewis G, Mavreas V (2003) Unexplained fatigue syndromes in a multinational primary care sample: specificity of definition and prevalence and distinctiveness from depression and generalized anxiety. Am J Psychiatry 160: 785-787. doi:. PubMed: .
    1. Tchekmedyian NS, Kallich J, McDermott A, Fayers P, Erder MH (2003) The relationship between psychologic distress and cancer-related fatigue. Cancer 98: 198-203. doi:. PubMed: .
    1. Stone AA, Shiffman S, Atienza AA, Nebeling L (2007) The science of real-time data capture: self-reports in health research. Oxford; New York: Oxford University Press.
    1. Singer JD, Willett JB (2003) Applied longitudinal data analysis: modeling change and event occurrence. Oxford; New York: Oxford University Press.
    1. Linhart H (1988) A test whether two AIC’s differ significantly. S Afr Stat J 22: 153-161.

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