An Internet-based program for depression using activity and physiological sensors: efficacy, expectations, satisfaction, and ease of use

Cristina Botella, Adriana Mira, Inés Moragrega, Azucena García-Palacios, Juana Bretón-López, Diana Castilla, Antonio Riera López Del Amo, Carla Soler, Guadalupe Molinari, Soledad Quero, Verónica Guillén-Botella, Ignacio Miralles, Sara Nebot, Berenice Serrano, Dennis Majoe, Mariano Alcañiz, Rosa María Baños, Cristina Botella, Adriana Mira, Inés Moragrega, Azucena García-Palacios, Juana Bretón-López, Diana Castilla, Antonio Riera López Del Amo, Carla Soler, Guadalupe Molinari, Soledad Quero, Verónica Guillén-Botella, Ignacio Miralles, Sara Nebot, Berenice Serrano, Dennis Majoe, Mariano Alcañiz, Rosa María Baños

Abstract

Purpose: Computerized cognitive behavioral therapy (CCBT) has been shown to be efficacious. Moreover, CCBT can be enhanced by using physiological and activity sensors, but there is no evidence about the acceptability of all these tools. The objective of this study is to examine the efficacy, expectations, satisfaction, and ease of use of an Internet-based CCBT program for preventing depression, with and without sensors (electroencephalography, electrocardiograhpy ECG, and actigraphy), in a high-risk population (unemployed men).

Patients and methods: Sixty participants at risk of depression (unemployed men) were randomly assigned to three experimental conditions: 1) intervention program (N=22), 2) intervention program plus sensors (N=19), and 3) control group (N=19). Participants completed depression, anxiety, positive and negative affect, and perceived stress measures. Furthermore, they also completed the measures for expectation, satisfaction, and the ease of use of the program.

Results: Results showed that the two intervention groups improved significantly more than the control group on the clinical variables, and the improvements were greater in the group that used sensors than in the group that did not use them. Furthermore, participants in both intervention groups scored high on expectations and satisfaction with the CCBT program (with and without sensors). The mean score for usability was 88 out of 100 (standard deviation =12.32). No significant differences were found between groups on any of these variables.

Conclusion: This is the first study to analyze the efficacy, expectations, satisfaction, and ease of use of an Internet-based program using physiological and activity sensors. These results suggest that an Internet program for depression with or without physiological and activity sensors is effective, satisfactory, and easy to use.

Keywords: Internet; depression; ease of use; efficacy; satisfaction; sensors.

Figures

Figure 1
Figure 1
Home module.
Figure 2
Figure 2
Activity report.
Figure 3
Figure 3
Calendar.
Figure 4
Figure 4
From left to right, the heart rate and heart rate variability sensor worn on the neck, the activity sensor worn on the ankle, and the brain wave sensor. Note: The red arrow indicates the correct position of the ACT sensor. The circular washer must be placed in the direction of the arrow. Abbreviations: ECG, electrocardiography; ACT, actigraphy; EEG, electroencephalography.
Figure 5
Figure 5
A comparison of mean SUS scores by quartile, adjective ratings and the acceptability of the overall SUS score. Abbreviation: SUS, System Usability Scale.
Figure 6
Figure 6
Flow chart. Abbreviations: IP, intervention program group; IP + S, intervention program plus sensors group; C, control group; BDI-II, Beck Depression Inventory II.

References

    1. Ferrari AJ, Charlson FJ, Norman RE, et al. Burden of depressive disorders by country, sex, age, and year: findings from the global burden of disease study 2010. PLoS Med. 2013;10(11):e1001547.
    1. Botella C, Moragrega I, Baños R, García-Palacios A. Online predictive tools for intervention in mental illness: the OPTIMI project. Stud Health Technol Inform. 2011;63:86–92.
    1. Barlow DH, Ellard KK, Fairholme CP, et al. Client Workbook. New York, NY: Oxford University Press; 2011. The Unified Protocol for Transdiagnostic Treatment of Emotional Disorders.
    1. Cuijpers P, Beekman ATF, Reynolds CF. Preventing depression: a global priority. JAMA. 2010;307:1033–1034.
    1. Gaston JE, Abbott MJ, Rapeey RM, Neary SA. Do empirically supported treatments generalize to private practice? A benchmark study of a cognitive-behavioural group treatment programme for social phobia. Br J Clin Psychol. 2006;45(1):33–48.
    1. Kaltenthaler E, Brazier J, De Nigris E, et al. Computerised cognitive behaviour therapy for depression and anxiety update: a systematic review and economic evaluation. Health Technol Assess. 2006;10(33):1–168.
    1. Kaltenthaler E, Parry G, Beverley C, Ferriter M. Computerised cognitive-behavioural therapy for depression: systematic review. Br J Psychiatry. 2008;193(3):181–184.
    1. Tate DF, Zabinski MF. Computer and Internet applications for psychological treatment: update for clinicians. J Clin Psychol. 2004;60(2):209–220.
    1. Andrews G, Cuijpers P, Craske MG, McEvoy P, Titov N. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One. 2010;5(10):e13196.
    1. Griffiths KM, Christensen H. Review of randomised controlled trials of internet interventions for mental disorders and related conditions. Clin Psychol. 2006;10:16–29.
    1. Postel MG, de Haan HA, De Jong CA. E-therapy for mental health problems: a systematic review. Telemed J E Health. 2008;14(7):707–714.
    1. Richards D, Richardson T. Computer-based psychological treatments for depression: a systematic review and meta-analysis. Clin Psychol Rev. 2012;32(4):329–342.
    1. Titov N, Dear BF, Schwencke G, et al. Transdiagnostic internet treatment for anxiety and depression: a randomised controlled trial. Behav Res Ther. 2011;49(8):441–452.
    1. Kaltenthaler E, Sutcliffe P, Parry G, Beverley C, Rees A, Ferriter M. The acceptability to patients of computerized cognitive behaviour therapy for depression: a systematic review. Psychol Med. 2008;38(11):1521–1530.
    1. Kay-Lambkin F, Baker A, Lewin T, Carr V. Acceptability of a clinician-assisted computerized psychological intervention for comorbid mental health and substance use problems: treatment adherence data from a randomized controlled trial. J Med Internet Res. 2011;13(1):e11.
    1. Mohr DC, Siddique J, Jin L, Fokuo JK. Interest in behavioral and psychological treatments delivered face-to-face, by telephone and by internet. Ann Behav Med. 2010;40(1):89–98.
    1. De Graaf LE, Huibers MJH, Riper H, Gerhards SAH, Arntz A. Use and acceptability of unsupported online computerized cognitive behavioral therapy for depression and associations with clinical outcome. J Affect Disord. 2009;116(3):227–231.
    1. Osgood-Hynes DJ, Greist JH, Marks IM, et al. Self-administered psychotherapy for depression using a telephone-accessed computer system plus booklets: an open U.S.-U.K. study. J Clin Psychiatry. 1998;59(7):358–365.
    1. Botella C, Gallego MJ, Garcia-Palacios A, Baños RM, Quero S, Alcañiz M. The acceptability of an Internet-based self-help treatment for fear of public speaking. Br J Guid Counc. 2009;37(3):297–311.
    1. Carrard I, Crépin C, Rouget P, Lam T, Van der Linden M, Golay A. Acceptance and efficacy of a guided internet self-help treatment program for obese patients with binge eating disorder. Clin Pract Epidemiol Ment Health. 2011;7:8–18.
    1. Cavanagh K, Shapiro DA, Van Den Berg S, Swain S, Barkham M, Proudfoot J. The acceptability of computer-aided cognitive behavioural therapy: a pragmatic study. Cogn Behav Ther. 2009;38(4):235–246.
    1. Gun SY, Titov N, Andrews G. Acceptability of Internet treatment of anxiety and depression. Australas Psychiatry. 2011;19(3):259–264.
    1. Wootton BM, Titov N, Dear BF, Spence J, Kemp A. The acceptability of Internet-based treatment and characteristics of an adult sample with obsessive compulsive disorder: an Internet survey. PLoS One. 2011;6(6):e20548.
    1. Bonato P. Clinical applications of wearable technology. Proc IEEE Eng Med Biol Soc. 2009;2009:6580–6583.
    1. Teng XF, Zhang YT, Poon CC, Bonato P. Wearable medical systems for p-Health. IEEE Rev Biomed Eng. 2008;1:62–74.
    1. Puiatti A, Mudda S, Giordano S, Mayora O. Smartphone-centred wearable sensors network for monitoring patients with bipolar disorder. Proc IEEE Eng Med Biol Soc. 2011;2011:3644–3647.
    1. Holzinger A, Searle G, Wernbacher M. The effect of previous exposure to technology on acceptance and its importance in usability and accessibility engineering. Univ Access Inform Soc. 2011;10:245–260.
    1. Holzinger A, Searle G, Wernbacher M. The effect of previous exposure to technology on acceptance and its importance in usability and accessibility engineering. UAIS. 2011;10:245–260.
    1. Cipresso P, Gaggioli A, Serino S, et al. Inter-reality in the evaluation and treatment of psychological stress disorders: the INTERSTRESS project. Stud Health Technol Inform. 2012;181:8–11.
    1. Gruenerbl A, Muaremi A, Osmani V, et al. Smartphone based recognition of states and state changes in bipolar disorder patients. IEEE J Biomed Health Inform. 2014;19(1):140–148.
    1. Spanakis EG, Chiarugi F, Kouroubali A, et al. Diabetes management using modern information and communication technologies and new care models. Interact J Med Res. 2012;1(2):e8.
    1. Tsipouras MG, Karvounis EC, Tzallas AT, et al. Automated knowledge-based fuzzy models generation for weaning of patients receiving ventricular assist device (VAD) therapy. Proc IEEE Eng Med Biol Soc. 2012;2012:2206–2209.
    1. Warmerdam L, Riper H, Klein M, et al. Innovative ICT solutions to improve treatment outcomes for depression: the ICT4Depression project. Stud Health Technol Inform. 2012;181:339–343.
    1. Riva G, Gaggioli A, Banos RM, Botella C, Wiederhold BK. Personal health systems for mental health: the European projects. Stud Health Technol Inform. 2011;163:496–502.
    1. Stjernswärd S, Ostman M. Illuminating user experience of a website for the relatives of persons with depression. Int J Soc Psychiatry. 2011;57(4):375–386.
    1. Algoe SB, Fredrickson BL. Emotional fitness and the movement of affective science from lab to field. Am Psychol. 2011;66(1):35–42.
    1. Ekers D, Richards D, McMillan D, Bland JM, Gilbody S. Behavioural activation delivered by the non-specialist: phase II randomised controlled trial. Br J Psychiatry. 2011;198(1):66–72.
    1. Botella C, Riva R, Gaggioli A, Wiederhold BK, Alcaniz M, Baños RM. The present and future of positive technologies. Cyberpsychol Behav Soc Netw. 2012;15(2):78–84.
    1. Holzinger A, Stocker C, Bruschi M, et al. On applying approximate entropy to ECG Signals for knowledge discovery on the example of big sensor data. Lect Notes Comput Sci. 2012;7669:646–657.
    1. Sun FT, Kuo C, Cheng HT, Buthpitiya S, Collins P, Griss ML. Activity-aware mental stress detection using physiological sensors. LNICST. 2012;76:282–301.
    1. Taliaferro LA, Rienzo BA, Pigg RM, Jr, Miller MD, Dodd VJ. Associations between physical activity and reduced rates of hopelessness, depression, and suicidal behavior among college students. J Am Coll Health. 2009;57(4):427–436.
    1. Stickel C, Fink J, Holzinger A. Enhancing universal access – EEG based learnability assessment. Lect Notes Comput Sci. 2007;4556:813–822.
    1. Nieber D, Schlegel S. Relationships between psychomotor retardation and EEG power spectrum in major depression. Neuropsychobiology. 1992;25(1):20–23.
    1. Dooley D, Catalano R, Wilson G. Depression and unemployment: panel findings from the Epidemiologic Catchment Area study. Am J Community Psychol. 1994;22(6):745–765.
    1. Ferrando L, Bobes J, Gibert J, Lecubrier Y. Mini International Neuropsychiatric Interview. In: Bobes J, Bousoño M, González MP, editors. Manejo de los trastornos mentales y del comportamiento en Atención Primaria. [Management of mental and behavioral disorders in primary care] Oviedo: Gofer; 1997. Spanish.
    1. Beck AT, Steer RA, Brown GK. Manual for the Beck Depression Inventory-II. San Antonio, TX: Psychological Corp; 1996.
    1. Sanz J, Navarro ME, Vázquez C. Adaptación española del Inventario para la Depresión de Beck-II (BDI-II): Propiedades psicométricas en estudiantes universitarios [Spanish adaptation of the Depression Inventory Beck-II (BDI-II): Psychometric properties in college students] Análisis y modificación de conducta. 2003;29:239–288. Spanish.
    1. Norman SB, Cissell SH, Means-Christensen AJ, Stein MB. Development and validation of an overall anxiety severity and impairment scale (OASIS) Depress Anxiety. 2006;23(4):245–249.
    1. Sandín B, Chorot P, Lostao L, Joiner TE, Santed MA, Valiente RM. Escalas Panas de afecto positivo y negativo: validación factorial y transcultural [Panas scales of positive and negative affect: factorial and transcultural validation] Psicothema. 1999;11(1):37–51. Spanish.
    1. Campo A, Bustos G, Romero A. Consistencia interna y dimensionalidad de la Escala de estrés percibido (EEP-10 y EEP-14) en una muestra de universitarias de Bogotá, Colombia Aquichan [Internal consistency and dimensionality of perceived stress scale (PSS-10 and PSS-14) in a university sample of Bogota, Colombia Aquichan] Red de Revistas Científicas de América Latina, el Caribe, España y Portugal. 2009;9(3):271–280. Spanish.
    1. Borkovec TD, Nau S. D. Credibility of analogue therapy rationales. J Behav Ther Exp Psychiatry. 1972;3(4):257–260.
    1. Brooke J. SUS: a “quick and dirty” usability scale. In: Jordan PW, Thomas B, Weerdmeester BA, McClelland IL, editors. Usability Evaluation in Industry. London, UK: Taylor & Francis; 1996. pp. 189–194.
    1. Bangor A, Kortum PT, Miller JT. An empirical evaluation of the system usability scale. Int J Hum Comput Interact. 2008;24(6):574–594.
    1. Cuijpers P, Donker T, Johansson R, Mohr DC, van Straten A, Andersson G. Self-guided psychological treatment for depressive symptoms: a metaanalysis. PLoS One. 2011;6(6):e21274.
    1. Spek V, Cuijpers P, Nyklícek I, Riper H, Keyzer J, Pop V. Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a metaanalysis. Psychol Med. 2007;37(3):319–328.
    1. Andersson G, Cuijpers P. Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cogn Behav Ther. 2009;38(4):196–205.
    1. Cuijpers P, Donker T, van Straten A, Li J, Andersson G. Is guided self-help as effective as face-to-face psychotherapy for depression and anxiety disorders? A systematic review and meta-analysis of comparative outcome studies. Psychol Med. 2010;40(12):1943–1957.

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