Restricted accessResearch articleFirst published online 2023-9
Quantifying self-quantification: A statistical study on individual characteristics and motivations for digital self-tracking in young- and middle-aged adults in Germany
This study presents a quantitative account of who uses or stops using digital self-tracking (ST). A representative sample of German adults aged 20–50 years (N = 1022) completed an online survey on their ST practices, personality traits and attitudes toward numbers, on sociodemographic characteristics, mental disorders (particularly bulimia, burnout syndrome, and depression) and somatic disorders. A descriptive statistical analysis was performed on differences between self-trackers and non-trackers. Among others, they differ regarding age, civil status, social class, presence of mental and/or somatic diagnoses, performance-pressure, and strive for competition. A consequent binary logistic regression analysis suggests perfectionism, a somatic diagnosis within the last 5 years, a diagnosis of bulimia in the past, as well as a present mental diagnosis to be significant predictors for ST, though the predictive value of the factors was relatively low.
AjanaB (2017) Digital health and the biopolitics of the Quantified Self. Digital Health. Epub ahead of print 1 February. DOI: 10.1177/2055207616689509.
2.
CharitsisVYngfalkAFSkålénP (2019) “Made to run”: biopolitical marketing and the making of the self-quantified runner. Marketing Theory19(3): 347–366.
ChoeEKLeeNBLeeB, et al. (2014) Understanding quantified-selfers’ practices in collecting and exploring personal data. In: Proceedings of the SIGCHI conference on human factors in computing systems, Toronto, ON, Canada, 26 April–1 May, pp. 1143–1152. New York: ACM.
5.
ClawsonJPaterJAMillerAD, et al. (2015) No longer wearing: investigating the abandonment of personal health-tracking technologies on craigslist. In: Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing, Osaka, Japan, 7–11 September, pp. 647–658. New York: ACM.
6.
CostaPTMcCraeRR (1992) Manual for the Revised NEO Personality Inventory (NEO-PI-R) and NEO Five-Factor Inventory (NEO-FFI). Odessa: Psychological Assessment Resources.
7.
CrawfordKLingelJKarppiT (2015) Our metrics, ourselves: a hundred years of self-tracking from the weight scale to the wrist wearable device. European Journal of Cultural Studies18(4–5): 479–496.
8.
DornquastCKrollLENeuhauserHK, et al. (2016) Regional differences in the prevalence of cardiovascular disease: results from the German Health Update (GEDA) from 2009–2012. Deutsches Ärzteblatt International113(42): 704–711.
9.
EhrenbergA (2009) The Weariness of the Self: Diagnosing the History of Depression in the Contemporary Age. Montreal, QC, Canada: McGill-Queen’s University Press.
10.
EikeyEVReddyMC (2017) “It’s definitely been a journey”: a qualitative study on how women with eating disorders use weight loss apps. In: Proceedings of the 2017 CHI conference on human factors in computing systems, Denver, CO, 6–11 May, pp. 642–654. New York: ACM.
11.
EpsteinDACarawayMJohnstonC, et al. (2016) Beyond abandonment to next steps: understanding and designing for life after personal informatics tool use. In: Proceedings of the 2016 CHI conference on human factors in computing systems, San Jose, CA, 7–12 May, pp. 1109–1113. New York: ACM.
12.
FioramontiL (2014) How Numbers Rule the World: The Use and Abuse of Statistics in Global Politics. London: Zed Books.
13.
FoxSDugganM (2013) Health Online 2013. Washington, DC: Pew Research Center, pp. 1–55.
14.
GerischBSalfeldBBeerbomC, et al. (2018) Optimisation by knife: on types of biographical appropriation of aesthetic surgery in late modernity. In: KingVGerischBRosaH (eds) Lost in Perfection: Impacts of Optimisation on Culture and Psyche. London: Routledge, pp. 131–145.
GimpelHNißenMGörlitzR (2013) Quantifying the quantified self: a study on the motivations of patients to track their own health. In: Proceedings of the 34th international conference on information systems, Milan, 15–18 December, pp. 1–16. Atlanta, GA: Association for Information Systems.
17.
GutierrezC (2016) The other self in free fall: anxiety and automated tracking applications. CM: Communication and Media11(38): 111–134.
18.
HeidemannCScheidt-NaveC (2017) Prävalenz, Inzidenz und Mortalität von Diabetes mellitus bei Erwachsenen in Deutschland: Bestandsaufnahme zur diabetes-surveillance. Journal of Health Monitoring2(3): 730.
19.
HeintzB (2010) Numerische Differenz. Überlegungen zu einer Soziologie des (quantitativen) Vergleichs. Zeitschrift für Soziologie39(3): 162–181.
20.
HoustonJHarrisPMcIntireS, et al. (2002) Revising the competitiveness index using factor analysis. Psychological Reports90(1): 31–34.
21.
JacobiFHöflerMStrehleJ, et al. (2015) Twelve-months prevalence of mental disorders in the German Health Interview and Examination Survey for Adults—Mental Health Module (DEGS1-MH): a methodological addendum and correction. International Journal of Methods in Psychiatric Research24(4): 305–313.
22.
KeelPKForneyKJ (2013) Psychosocial risk factors for eating disorders. International Journal of Eating Disorders46(5): 433–439.
23.
KentR (2018) Social media and self-tracking: representing the “health self.” In: AjanaB (ed.) Self-Tracking: Empirical and Philosophical Investigations. Cham: Palgrave Macmillan, pp. 61–76.
24.
KingVGerischBRosaH (2018) Lost in Perfection: Impacts of Optimisation on Culture and Psyche. London: Routledge.
25.
KingVGerischBSchreiberJ (2020) “. . . To really have everything completely perfect”: on the psychodynamics of contemporary forms of body optimization. Psychoanalytic Psychology37(2): 148–157.
26.
KingVGerischBRosaH, et al. (2019) Psychische Bedeutungen des digitalen Messens, Zählens und Vergleichens. Psyche73(9): 744–770.
27.
KrügerS (2018) The authoritarian dimension in digital self-tracking: containment, commodification, subjugation. In: KingVGerischBRosaH (eds) Lost in Perfection: Impacts of Optimisation on Culture and Psyche. London: Routledge, pp. 85–104.
28.
KuhlJKazénM (2009) Persönlichkeits-Stil- und Störungs-Inventar: PSSI; Manual. Göttingen: Hogrefe.
29.
LuptonD (2016) The diverse domains of quantified selves: self-tracking modes and dataveillance. Economy and Society45(1): 101–122.
30.
LuptonD (2017) Digital Health: Critical and Cross-Disciplinary Perspectives. London: Routledge.
31.
LutzSSivakumarG (2020) Leaking the secret: women’s attitudes toward menstruation and menstrual-tracker mobile apps. Gender, Technology and Development24(3): 362–377.
32.
LyallB (2019) Fitness for sale: the value of self-tracking in secondhand exchange. The Information Society35(3): 109–121.
33.
MaltsevaKLutzC (2018) A quantum of self: a study of self-quantification and self-disclosure. Computers in Human Behavior81: 102–114.
34.
MaskeUERiedel-HellerSGSeiffertI, et al. (2015) Prevalence and comorbidity of self-reported diagnosis of burnout syndrome in the general population—results of the German Health Interview and Examination Survey for Adults (DEGS1). Psychiatrische Praxis43(1): e1.
35.
MauS (2019) The Metric Society: On the Quantification of the Social. Hoboken, NJ: John Wiley & Sons.
36.
MennickenAEspelandWN (2019) What’s new with numbers? Sociological approaches to the study of quantification. Annual Review of Sociology45: 223–245.
37.
MoorePRobinsonA (2016) The quantified self: what counts in the neoliberal workplace. New Media & Society18(11): 2774–2792.
38.
MorozovE (2013) To Save Everything, Click Here: The Folly of Technological Solutionism. New York: PublicAffairs.
39.
MullerJZ (2018) The Tyranny of Metrics. Princeton, NJ: Princeton University Press.
40.
NeffGNafusD (2016) Self-Tracking. Cambridge, MA: The MIT Press.
41.
ParéGLeaverCBourgetC (2018) Diffusion of the digital health self-tracking movement in Canada: results of a national survey. Journal of Medical Internet Research20(5): e177.
42.
PettinicoGMilneGR (2017) Living by the numbers: understanding the “quantification effect.”Journal of Consumer Marketing34(4): 281–291.
43.
PolsJWillemsDAanestadM (2019) Making sense with numbers. Unravelling ethico-psychological subjects in practices of self-quantification. Sociology of Health & Illness41: 98–115.
44.
RégnierFChauvelL (2018) Digital inequalities in the use of self-tracking diet and fitness apps: interview study on the influence of social, economic, and cultural factors. JMIR mHealth and uHealth6(4): e101.
45.
RettbergJW (2014) Seeing Ourselves Through Technology: How We Use Selfies, Blogs and Wearable Devices to See and Shape Ourselves. Basingstoke: Palgrave Macmillan.
46.
RettbergJW (2018) Apps as companions: how quantified self apps become our audience and our companions. In: AjanaB (ed.) Self-Tracking: Empirical and Philosophical Investigations. Basingstoke: Palgrave Macmillan, pp. 27–42.
47.
SandersR (2017) Self-tracking in the digital era: biopower, patriarchy, and the new biometric body projects. Body & Society23(1): 36–63.
48.
SchienkiewitzAMensinkGKuhnertR, et al. (2017) Übergewicht und Adipositas bei Erwachsenen in Deutschland. Journal of Health Monitoring2(2): 21–28.
49.
SelkeS (2016) Rational discrimination and lifelogging: the expansion of the combat zone and the new taxonomy of the social. In: SelkeS (ed.) Lifelogging. Wiesbaden: Springer, pp. 345–372.
50.
SharonT (2017) Self-tracking for health and the quantified self: re-articulating autonomy, solidarity, and authenticity in an age of personalized healthcare. Philosophy and Technology30(1): 93–121.
51.
SharonTZandbergenD (2017) From data fetishism to quantifying selves: self-tracking practices and the other values of data. New Media & Society19(11): 1695–1709.
Statistisches Bundesamt (2019) Mikrozensus 2017—Fragen zur Gesundheit. Wiesbaden: Statistisches Bundesamt.
55.
SteppuhnHKuhnertRScheidt-NaveC (2017a) 12-Monats-Prävalenz der bekannten chronisch obstruktiven Lungenerkrankung (COPD) in Deutschland. Journal of Health Monitoring2(3): 46–54.
56.
SteppuhnHKuhnertRScheidt-NaveC (2017b) 12-Monats-Prävalenz von Asthma bronchiale bei Erwachsenen in Deutschland. Journal of Health Monitoring2(3): 36–45.
57.
ZillienNFröhlichG (2018) Reflexive Selbstverwissenschaftlichung. Eine empirische Analyse der digitalen Selbstvermessung. In: MämeckeTPassothJ-HWehnerJ (eds) Bedeutende Daten. Wiesbaden: Springer, pp. 233–249.
