RESEARCH/Systematic review

Web-based interventions for comorbid depression and chronic illness: a systematic review

Journal of Telemedicine and Telecare 2015, Vol. 21(4) 189–201 ! The Author(s) 2015 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav DOI: 10.1177/1357633X15571997 jtt.sagepub.com

Ekaterina Charova1, Diana Dorstyn1, Phillip Tully2 and Oskar Mittag3

Summary Web-based interventions offer potential benefits for managing and treating depression in the context of chronic physical illness, however their use with this population has yet to be quantitatively assessed. The present systematic review examined the biopsychosocial data from 11 independent studies (N ¼ 1348 participants), including randomised controlled and quasi-experimental designs most commonly performed with diabetes and multiple sclerosis. Study quality was evaluated using the Downs and Black (1998) index, with most studies being statistically underpowered although internal validity was demonstrated. Treatment outcomes were quantified using Cohen’s d effect sizes. Results indicated significant short-term improvements in depression severity (dw ¼ 0.36, CI ¼ 0.20-0.52, p < 0.01), in addition to quality of life, problem-solving skills, functional ability, anxiety and pain-related cognitions (d range ¼ 0.23 to 1.10). Longer-term outcomes could not be determined based on the limited data. Further robust studies are required before wider adoption of web techniques takes place. Keywords E-health, online health, self care, telehealth Date received: 7 June 2014; accepted: 13 January 2015

Targeted depression treatment constitutes a critical component of chronic illness management, with up to 50% of adults diagnosed with cancer, diabetes mellitus, heart disease, rheumatoid arthritis or stroke experiencing a moderate to severe range of cognitive and behavioural symptoms consistent with Major Depressive Disorder.1,2 However, barriers to accessing psychological treatments have been identified. This includes knowledge and beliefs (i.e. stigma) about mental disorders, illness symptoms (e.g. neuropathic pain) which impede therapy engagement, in addition to service access issues for those who are geographically remote.3-4 In this context, web-based psychological interventions offer an efficient and accessible means of service delivery.5,6 Meta-analytic and systematic reviews of web-based psychotherapies for the general population have demonstrated treatment efficacy. Computerized cognitive behaviour therapy (CBT), in particular, has been found to contribute to moderate improvements in depressive symptomatology in addition to comparability with face-to face psychotherapy.7-13 Interactive and self-guided web interventions in chronic illness settings have also demonstrated psychosocial benefits, including improved quality of life.14-16 However, available reviews have focused on

medical outcomes14 or have included a broad range of psychosocial interventions as opposed to targeted treatment of depression.15-16 As individuals with comorbid depression and chronic illness are at a high risk of mortality, morbidity and increased healthcare costs, a specific focus on this patient group is warranted.1,2 The present quantitative review provides an initial examination of targeted web-based interventions for depression among chronic illness groups. Specific aims are to: 1) examine the methodological quality of the available empirical literature; 2) quantify the short- and longerterm biopsychosocial effects of web-based interventions;

1

University of Adelaide School of Psychology, Adelaide, South Australia, Australia 2 University of Adelaide, Discipline of Medicine, Adelaide, South Australia, Australia 3 Medical Center - University of Freiburg, Institute for Quality Management and Social Medicine, Germany Corresponding author: Diana Dorstyn, University of Adelaide, School of Psychology, Adelaide, South Australia, 5005 Australia. Email:[email protected]

190 and 3) identify treatment factors that impact on consumer acceptability and satisfaction.

Methods Inclusion and exclusion criteria This review is registered with PROSPERO (registration CRD42014013938). Eligible studies had to be published in English in a peer-reviewed journal from 1990 (coinciding with introduction of worldwide web in 1991) until 2014.17 Participants. Studies had to target adult participants (aged 18 years or older) with a primary diagnosis of chronic illness and comorbid depression. Chronic illness was defined as a physical health problem that involves a complex causality, long latency, prolonged and often progressive course with resulting functional impairment.1,4,18 This included non-communicable diseases (e.g., cancer, cardiovascular disease), communicable diseases (e.g., HIV/ AIDS), and impairments in body structure (e.g., joint disorders). Studies with participants in cancer remission and those at risk of developing cardiovascular disease were also eligible, as they have been previously considered sufficiently homogenous.19,20 Depression was defined in accordance with diagnostic criteria (Diagnostic and Statistical Manual of Psychiatric Disorders version III-R and later, International Classification of Disease version 9 and later),21,22 or depressive symptomatology (i.e. above a predefined cut-off on a validated self-report or clinician measure). Web-based interventions. The examined web-based interventions required the following components: (a) program content (i.e. psychoeducation and skills training, guided by psychological theory); (b) multimedia use/choices; (c) provision of interactive web-based activities; and d) provision of guidance and supportive feedback, either through inbuilt program content (i.e. self-guided programs), or via human assistance.23 Eligible interventions had to target depression symptomatology, with the specific intent of producing emotional, behavioural, and cognitive change.23 Study design. Intervention studies with a repeated measures design, including randomised controlled trials and quasi experimental studies, were eligible. Quasi experimental designs help to minimise some of the practical and ethical difficulties in implementing a no-treatment control condition within a clinical setting and are considered advantageous in terms of their high external validity.24

Search strategy The Pubmed, PsycINFO, Scopus, and Embase databases were searched using logic grids developed with the assistance of a research librarian (see online supplementary

Journal of Telemedicine and Telecare 21(4) material). The following topic-specific journals were also searched: Journal of Telemedicine and Telecare; Telemedicine and e-health; Cyberpsychology and Behaviour; Cyberpsychology, Behavior, and Social Networking; International Journal of Medical Informatics; Electronic Journal of Health Informatics; Journal of the International Society for Telemedicine and eHealth; Journal of Technology in Human Services. Additional studies were located from the reference lists of identified studies. The database search was conducted by the first author (E.C), with results confirmed by the second and third authors (D.D, P.M) to minimize potential selection bias.25 After applying the aforementioned criteria, eleven articles were deemed eligible for inclusion in this review (Figure 1).19,26-35

Data extraction Consistent with the PRISMA guidelines36 the following data were extracted from each study: research design (e.g. randomised vs quasi-experimental, independent versus dependent groups design); sample characteristics (e.g. sample N, medical diagnosis, mean age, time since diagnosis); and treatment characteristics (e.g. session frequency, duration). Where available, statistics necessary for calculating treatment effects (i.e. means and standard deviations) were also extracted, with two studies26,27 providing additional data on request. Data extraction was conducted by the first author (E.C).

Quality evaluation The 27-item Quality Index (QI)37 was utilised to identify methodological bias (i.e. study power, reporting quality, external and internal validity). Each item is scored as 0 (not present) or 1 (present), with item scores totalled. Additional points are allocated for adequate power calculations and detail relating to potential confounders in the selection of study participants, resulting in a maximum quality score of 32. The QI has demonstrated inter-rater reliability (r ¼ 0.75).37Quality ratings were independently undertaken by the first and second authors (E.C., D.D.), with good inter-rater consistency demonstrated (Spearman’s rho ¼ 0.87, p < 0.0138). Consensus quality scores were subsequently achieved through discussion (see online supplementary material).

Effect size estimation Cohen’s d effect sizes39,40 were calculated to quantify treatment outcomes for ten studies, with Thompson et al34 providing binary data. As per Cohen’s39 criteria, d values of 0.2, 0.5, and 0.8 indicated small, medium, and large treatment effects. The computation of d involved several stages. First, effect sizes were calculated for each individual outcome measure utilised by a study. Where available, intentionto-treat (ITT) data were utilised as recommended for

Charova et al.

191

Conference (poster) abstract with no data available.

Figure 1. Flowchart of article selection.

psychotherapy research.41 Second, the direction of d was standardised so that a positive value indicated that the web-based intervention was beneficial. Third, 95% confidence intervals (CI) were calculated for all effect sizes, with CIs that do not include the value of zero considered clinically significant.42 Effect sizes for the primary outcome, depression, were separately examined using the I2 heterogeneity index (random effects model).43 This involved pooling and weighting the individual d’s for each study by their inverse variance. Cooper et al26 utilised two self-report measures of depression, which were averaged for the I2 analysis in order to ensure data independence.42 I2 is expressed as a percentage, with values < 40% representing an appropriate level of

methodological and clinical variability.43 Finally, publication bias was addressed by calculating fail-safe N (Nfs).42 The Nfs statistic estimates the number of unpublished studies with non-significant results (d ¼ 0.20)44 which would be required to exist in order to overturn the findings. Larger Nfs values indicate a greater likelihood that such studies are unlikely to exist.

Results Participant characteristics A pooled sample of 1,348 adults contributed to this review (Table 1). The average age was 49 years (SD ¼ 10,

Country

Netherlands

United States

United Kingdom

United States

United Kingdom

Australia

Norway

United States

Australia

Lead author (year)

Boeschoten (2012)

Bond (2010)

Bundy (2013)

Cohn (2014)

Cooper (2011)

Dear (2013)

Drozd (2013)

Duffecy (2013)

Glozier (2013)

CVD

Cancer

HIV

Chronic pain

Multiple Sclerosis

Diabetes

Psoriasis

Diabetes

Multiple Sclerosis

Diagnosis

67

31

562

None

HADS 5 8

K-10 5 16

62

24

BDI-II 5 14

Nil

53

135

Nil

Nil

62

44

BDI-II 5 16

Nil

N

Depression criteria

Sample characteristics

Table 1. Summary of included studies (N ¼ 11 studies).

58 (7)

50 (-)

48 (9)

49 (13)

45 (8)

54 (-)

45 (13)

67 (6)

45 (12)

Mean age (SD)

–

–

Control: 11.1 (6.8) Treatment: 11.6 (6.6)

7.4 (8.1)

–

–

14 (0-30)

Control: 17.8 (11.7) Treatment: 16.1 (10.5)

5 (2-40)

Years since diagnosis (SD/range)

Methodology

PHQ9

HADS

CES-D

PHQ9

BDI-II; MSIS-29

CES-D

HADS

CES-D

BDI-II

Depression outcome

Online education

health

Online health education and monitoring

Usual care

Waitlist

Usual care

Waitlist

Waitlist

Usual care

Nil

Control

Nil

Nil

3 months

3 months

13 weeks

Nil

6 months

Nil

Nil

Follow up ‘Allesondercontrole’ 5 weeks/ 5 sessions Weekly e-mails Trainee psychologists Problem Solving Therapy

eTIPs 6 weeks/ 6 sessions/ weekly Therapy modules plus education CBT

‘PainCourse’ 8 weeks/ 5 sessions/ weekly Weekly telephone calls, e-mail Psychologist CBT

(continued)

 ‘E-couch’  12 weeks/ 12 sessions (30-60min)/ weekly

 ‘Mood-Manager’  8 weeks/ 16 sessions (10-15min)/ biweekly  Modules, peer discussion board  CBT

 ‘Avanti’  1 month/ 14 sessions  Meta-cognitive/ Positive Psychology

    

 ‘Beating the Blues’  8 weeks/ 8 sessions (50min)/ weekly  CBT

 ‘DAHLIA’  8 weeks/ 5 sessions/ weekly  Stress and Coping Theory/ Broadenand- Build Theory

   

 6 months/ weekly  Instant messaging, chat, e-mail, bulletin board  Psychologist, Clinical nurse, peers

    

Intervention details

192 Journal of Telemedicine and Telecare 21(4)

Measure abbreviations: BDI-II: Beck Depression Inventory II; HADS: Hospital Anxiety Depression Scale; K-10: Kessler Psychological Distress Scale; CES-D: Center for Epidemiological Studies Depression Scale; PHQ9: Patient Health Questionnaire; MSIS-29: Multiple Sclerosis Impact Scale; mBDI: Beck Depression Inventory (modified). (-) indicates data not provided or available.

‘Coping with Depression’ 8 sessions Modules, standardised email feedback CBT     1 month CES-D 255 CES-D 5 16 Netherlands van Bastelaar (2011)

Diabetes

50 (12)

Type 1: 21 (13) Type II: 9 (8)

Waitlist

 ‘UPLIFT’  8 weeks/ 8 sessions (60min)/ weekly  Video instruction, skill building, discussion board, homework  Psychologist, peer  CBT 8 weeks  Usual care  Phone intervention mBDI – 53 CES-D > 13 United States Thompson (2010)

Epilepsy

 Phone call, email, text messaging  CBT/Interpersonal psychotherapy

N Diagnosis Country

Depression criteria

36 (-)

Years since diagnosis (SD/range)

193

Lead author (year)

Table 1. Continued

Sample characteristics

Mean age (SD)

Methodology

Depression outcome

Control

Follow up

Intervention details

Charova et al.

range ¼ 20 to 91 years), with the majority of participants being female (67%, n ¼ 903). The most common diagnoses were diabetes and multiple sclerosis, which were examined by two or more studies. Average time since diagnosis was 12 years (SD ¼ 4.6), although five studies did not provide this detail. Five studies based participant recruitment on clinical cut-off scores indicative of problematic depression from baseline self-report, clinical-interview or both.26,28,33,34,35 Duffecy et al.19 removed such criteria to aid recruitment.

Intervention characteristics Five web-based interventions had been designed to manage depression symptoms in the general population,19,26,27,31,33 with the remaining studies modified to incorporate illness-specific information and clinical examples. Most treatments involved an individual format, with only two group interventions evaluated.29,34 Interventions were time-limited, with treatment involving an average of nine modules (SD ¼ 4, range ¼ 5 to 16) delivered on a weekly or biweekly basis over 1 to 6 months (median ¼ 8 weeks). CBT was the primary framework, with four studies utilising registered or trainee psychologists to promote completion of session material and homework tasks.28,32,33,35 Additional online resources included a bulletin board, peer-led discussion forum and instructional videos.

Study quality Quality scores ranged from 14 to 29 out of a maximum 32 (mean ¼ 19, SD ¼ 4.6; see online supplementary material). Aside from three studies,30,33,35 most were underpowered due to the use of small sample sizes. Reporting quality was adequate with key hypotheses, outcome measures and findings described in addition to protocols for addressing potential suicide risks. Five studies26,27,28,33,35 discussed the characteristics of participants who withdrew and the consequent socio-demographic disparities between completers and non-completers, however recruitment difficulties and limited detail relating to the source population contributed to poor external validity. Internal validity was achieved through the use of random allocation to treatment conditions (N ¼ 10 studies), although the method of randomization was not always clearly described.

Treatment effectiveness The nine studies providing depression outcome data contributed to a positive and medium dw (Figure 2). A more conservative treatment effect was noted when randomised controlled trials were examined only (dw ¼ 0.31; CI ¼ 0.17 to 0.45; p < 0.01). Although appropriate heterogeneity was demonstrated (I2 ¼ 19.73%, CI ¼ 0.00–71.74%), the associated CI suggests a wide spectrum of possible degrees of heterogeneity (i.e. mild to substantial).

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Author (year)

Measure

Boeschoten, 2012

BDI

Cooper, 2011

Weight %

p

0.56 (0.25; 0.87)

17.94%

0.00

BDI/PHQ-9

0.92 (0.06; 1.78)

3.24%

0.04

Dear, 2013

PHQ-9

0.74 (0.25; 1.23)

8.98%

0.00

Glozier, 2013

PHQ-9

0.30 (0.10; 0.50)

31.02%

0.00

Bond, 2010

CES-D

0.32 (-0.17; 0.81)

8.98%

0.20

Cohn, 2014

CES-D

0.40 (-0.23; 1.03)

5.83%

0.21

Drozd, 2013

CES-D

-0.08 (-0.57; 0.41)

8.98%

0.75

Bundy, 2013

HADS-D

0.15 (-0.30; 0.60)

10.32%

0.51

Duffecy, 2013

HADS-D

0.22 (-0.49; 0.93)

4.71%

0.54

100.00%

0.00

Synthesis

d (CI)

0.36 (0.20; 0.52)

-1

-0.5

0

0.5

1

1.5

2

d

Figure 2. Forest plot of short-term depression outcomes. Measure abbreviations: BDI: Beck Depression Inventory; PHQ9: Patient Health Questionnaire; CES-D: Center for Epidemiological Studies Depression Scale; HADS: Hospital Anxiety Depression Scale – depression subscale. Forest plot prepared using Meta-analysis with Interactive eXplanations software version 2.0.1.4. BiostatXL. Retrieved from http://www.meta-analysis-madeeasy.com

Improvements in depression symptomatology were noted by individuals with multiple sclerosis who completed web-based problem-solving therapy28 or CBT.26 Individuals with chronic arthritic pain also reported treatment gains following web-based CBT supplemented with therapist contact via telephone, moreso than peers who accessed usual care.32 Controlled comparative outcome studies were limited, although web-based CBT produced greater treatment effects than a health information package for individuals with high cardiovascular risk.33 These findings are consistent with the proportional data provided by Thompson et al,34 with mean Beck Depression Inventory (BDI) scores decreasing by 59% among adults with epilepsy who accessed a mindfulness-based cognitive program compared to waitlist controls (15% reduction in pre- to post- BDI), and no statistically significant differences noted between web- and telephone-delivered programs. These results need to be interpreted cautiously given the sparse dataset. Web-based interventions additionally contributed to improved psychosocial and illness-specific outcomes for diabetes, chronic pain, cardiovascular disease and multiple sclerosis (Table 2). This included short-term gains in social support ratings, self-efficacy and problem-solving abilities, in conjunction with reduced pain management, degree of functional impairment and anxiety symptoms.

These treatment estimates were, however, characterised by publication bias. Four controlled studies provided 1 to 3 month followup data, contributing to a pooled and non-significant d (Figure 3).26,27,32,35 Depressed patients in van Bastelaar’s evaluation of web-based CBT35 reported a small, albeit significant improvement (d ¼ 0.29, CI ¼ 0.17 -0.40) in diabetes- specific distress in comparison to waitlist controls (Figure 3). Descriptive data provided by Thompson et al34 also indicated a persistent reduction in depression symptoms at 8 weeks post-treatment, although the authors acknowledge that their results were preliminary (N ¼ 13). Additional psychosocial outcomes were not maintained following the cessation of treatment (Table 3). Treatment adherence and satisfaction. Treatment completion rates varied from 30% to 93%, with Dear et al32 enhancing recruitment by utilising public advertisements in local newsletters or newspapers and online media. Cited reasons for dropout included technological difficulties, socio-environmental stressors (e.g. job redundancy, relationship difficulties), illness-related problems and conflicting time commitments.26,28 A higher completion rate was found among diabetic participants recruited online in comparison to in-person recruitments from a community clinic,31 whilst van Bastelaar and colleagues35 noted

Affect

Pain cognitions

Anxiety

Physical function & disability

Problem solving

Pain Responses Self-Statements - Catastrophising - Coping TAMPA Scale of Kinesiophobia Short Form 36 – mental component Positive and Negative Affect Schedule

Hospital Anxiety and Depression Scale- anxiety

Psoriasis Area and Severity Index WHODAS II Generalised Anxiety Disorder – 7 item

Roland Morris Disability Questionnaire Short Form 36 - physical functioning

Diabetes Support Scale Pain Self-efficacy Questionnaire Diabetes Empowerment Scale Multiple Sclerosis Impact Scale – 29 item - Psychological - Physical Problem Areas in Diabetes Scale Dermatology Life Quality Index EuroQol 5 item Social Problem Solving Inventory Revised - Negative Problem Orientation - Avoidance - Positive Problem Orientation - Impulsivity/Carelessness - Rational Problem Solving Wisconsin Brief Pain Questionnaire

Social support Self-efficacy

Quality of life

Outcome measure

Construct

1 1 1 1 1 1 1 1 1 1 1

1 1 1

T vs C T vs C Treatment T vs C T vs T T vs C T vs C T vs T T vs C Treatment T vs C T vs C T vs C T vs C T vs C

1

1 1 1 1

T vs C T vs C Treatment Treatment

T vs C

1 1 1 1

vs vs vs vs

Nstudies

C C C C

T T T T

Design

62 22 67

62 22 44 85 562 62 23 562 78 44 62

62

62 76 44 44

62 62 62 23

Nparticipants

ITT Completers ITT

ITT Completers ITT Completers ITT ITT Completers ITT Completers ITT ITT

ITT

– Completers ITT ITT

– ITT – Completers

Analysis

Table 2. Short-term treatment effects for standardised measures administered pre- and immediately post-intervention.

0.54* 0.54* 0.53* 0.49 0.30

0.59* 0.13 0.00 0.06 0.05 0.61* 0.27 0.23* 0.36 0.28

0.44* 0.18 0.07 0.04 0.02 0.68*

0.81 0.17 0.45 0.44 0.10

1.10* 1.05* 0.68*

d

0.05 0.05 0.05 0.36 0.18

0.10 0.96 0.30 0.38 0.15 0.12 0.54 0.03 0.08 0.02

0.09 0.11 0.23 0.26 0.28 0.18

0.03 0.64 0.04 0.00 0.20

0.58 0.55 0.18

Lower

95% CI

1.03 1.03 1.01 1.34 0.78

1.08 0.70 0.30 0.50 0.25 1.10 1.08 0.43 0.80 0.58

0.79 0.47 0.37 0.34 0.32 1.18

1.65 0.98 0.94 0.88 0.40

1.62 1.57 1.18

Upper

2 2 2 1 1

2 0 0 0 0 2 0 0 1 0

1 1 0 0 0 2

3 0 1 1 0

5 4 2

Nfs

(continued)

Dear 2013 Cooper 2011 Drozd 2013

Dear 2013 Cooper 2011 Boeschoten 2012 Bundy 2013 Glozier 2013 Dear 2013 Cooper 2011 Glozier 2013 Bundy 2013 Boeschoten 2012 Dear 2013

Dear 2013

Bond 2010 Bundy 2013 Boeschoten 2012 Boeschoten 2012

Bond 2010 Dear 2013 Bond 2010 Cooper 2011

Lead author

Charova et al. 195

Completers ITT ITT 42 562 67 1 1 1 T vs C T vs T T vs C Stress Compliance Life satisfaction

Abbreviations: d: Cohen’s effect size; CI: 95% confidence interval; T vs C: treatment vs. wait list or usual care; T vs T: treatment vs comparative online condition; Treatment: single group (treatment only); Completers: d calculated on treatment completers; ITT: d calculated on intention-to-treat data. *significant treatment effect: CI 6¼ 0.

Cohn 2014 Glozier 2013 Drozd 2013 0.65 0.64 0.78 0.07 0.55 0.59 0.60 0.46 0.47 0.41 0.03 0.02 0.16 0.27 0.07

T vs C

Differential Emotions Scale - negative affect - positive affect Perceived Stress Scale Medical Outcomes Study - patient adherence Satisfaction with Life Scale

1

42

Completers

Lower d Design Outcome measure Construct

Table 2. Continued

Nstudies

Nparticipants

Analysis

95% CI

Upper

0 0 0 0 0

Nfs

Cohn 2014

Journal of Telemedicine and Telecare 21(4)

Lead author

196

anxiety disorders to be prevalent among non-completers. Treatment adherence was formally evaluated by two studies. Duffecy and colleagues19 reported a trend-level significance for a larger number of website log-ins among treatment participants, whereas Cohn et al.31 suggested that the time commitment required by their coping skills intervention (e.g. structured exercises, homework) may have contributed to reduced log-ins. Participants valued the inclusion of therapist support, psycho-education with illness-specific examples, structured therapy framework and opportunity for peer discussion. Negative feedback was noted by one study – with the BDI identified as an inaccurate reflection of individuals’ affective experience with multiple sclerosis.26

Discussion This review consolidates the findings from 11 studies investigating the effectiveness of web-based interventions for comorbid depression in populations with chronic illness. Although immediate improvements in depression symptom severity and broader psychosocial outcomes were identified, longer-term treatment effects could not be established. These findings need to be considered in the context of study quality, with limited information on randomization strategies and treatment blinding – qualities that are considered cornerstones of treatment evaluation.45 These results are consistent with previous research supporting the short-term efficacy of web-based depression treatments in the general population,7,8,11,13 lending support to the notion that web-based interventions have potential as a psychology service delivery option for chronic illness groups. The broader effects of the webbased interventions on perceived frequency and quality of social support networks, self-efficacy and anxiety, are also important for individuals with a chronic health condition due to the high comorbidity with psychosocial detriments, contributing to lowered quality of life.1,2 It follows that modifying these psychosocial factors, which may themselves predispose or enable improved health care utilization, can contribute to improved self-management and participation in web-based psychological services.46-48 The relationship between depression outcomes and degree of therapist involvement requires further evaluation, with few studies providing direct comparisons (e.g. clinician vs self-guided intervention). Cooper et al’s26 selfmanagement program reported the largest treatment gain, however this finding was based on less than 30 participants, precluding generalizability of the data. The broader web-based intervention literature favours supported intervention over self-guided help, although there are some exceptions to this.15,16,51,52 Further research is therefore needed to clarify the components of effective self-guided interventions, including the availability of technological support in maintaining participant engagement.49,50 Similarly, the need for illness-specific modifications in

Charova et al.

197

Measure

Cooper, 2011

BDI/PHQ-9

-0.01 (-0.93; 0.91)

3.91%

0.98

Dear, 2013

PHQ-9

0.13 (-0.20; 0.46)

29.92%

0.44

Drozd, 2013

CES-D

-0.04 (-0.51; 0.43)

15.01%

0.87

van Bastelaar, 2011

CES-D

0.29 (0.04; 0.54)

51.16%

0.03

0.18 (0.00; 0.36)

100.00%

0.05

Synthesis

d (CI)

Weight %

Author (year)

p

-1.5

-1

-0.5

0

0.5

1

1.5

d

Figure 3. Forest plot of follow-up depression outcomes. Measure abbreviations: BDI: Beck Depression Inventory; PHQ9: Patient Health Questionnaire; CES-D: Center for Epidemiological Studies Depression Scale.

depression treatments3 was not clearly reflected in the findings of this review. Practical considerations surrounding recruitment and retention were highlighted, with illness-related issues and time demands cited as key reasons for attrition.10,53 It may also be that the delivery of psychological services in a technological environment introduces a selection bias – potentially excluding those sub-groups with reduced web literacy such as the elderly and those from disadvantaged socio-economic backgrounds.54 Study evaluation identified the need to consider alternative research designs, with the ‘gold standard’ randomised controlled trial and double blinding difficult to achieve within a psychotherapeutic setting.45 This might include the case series design, which focuses on individual treatment change over time,55 alongside qualitative research to determine aspects such as patient acceptability. Generalisability of the current findings can also be improved in future web-based psychotherapy research by providing a clear description of sample and study characteristics, including demographic details of completers and non-completers and potential treatment confounders.37,56 This might include the type and extent of one’s prior engagement with psychotherapeutic services and current psychotropic management. Adherence rates were also not consistently measured or documented across available studies, making it difficult to understand the level of engagement with the examined interventions, and therefore validity of outcomes. Donkin and colleagues57 suggest the use of a composite measure

encompassing activity completion, time spent online, and active engagement with the treatment material as a best measure of adherence. It would also be advantageous for future research to utilise common validated measures in order to allow data pooling. Although this is challenging given variation in the prevalence of depression and other physical, cognitive and psychosocial outcomes inherent in the chronic illness population,58 tools such as the depression metric of the National Institutes of Health (NIH) Patient-Reported Outcomes Measurement Information System (PROMIS)59 have demonstrated reliability with medical populations. The PROMIS, in particular, maps onto scores from existing depression measures (e.g. BDI), allowing for ease of comparison. 60

Limitations Caution is needed in drawing conclusions from this review. In particular, measurement issues can be substantial when making between-study comparisons among a small number of trials. This includes differences in the scaling and precision of measurement across the 26 outcome measures utilised by the identified studies, despite some focussing on the same psychosocial concept.61 These measurement problems are partly overcome by using standardised estimates of effect magnitude, such as Cohen’s d, although d estimates based on single-group designs may be inflated.41 In addition, sample differences in depression criteria (i.e. different cut-off scores at which

TAMPA Scale of Kinesiophobia Pain Responses Self-Statements - Coping - Catastrophising Pain Self-efficacy Questionnaire Satisfaction with Life Scale Treatment T vs C

1 1

1 1 1 1

1 1 1 1 1

1

Nstudies

31 67

31 21 31 31

31 31 19 67 20

19

Nparticipants

ITT ITT

ITT Completers ITT ITT

ITT ITT Completers ITT Completers

Completers

Analysis

3 months 3 months

3 months 13 weeks 3 months 3months

3 months 3 months 13 weeks 3 months 13 weeks

13 weeks

Follow-up

0.59 1.09 0.08 0.71 0.13 0.14 0.18 0.17 0.46

0.20 0.16 0.17 0.02

0.03 0.15 0.93 0.53

0.04

Lower

0.29 0.21 0.26 0.15 0.21

0.32 0.20 0.30 0.05

0.61

d

95% CI

0.54 0.50 0.51 0.50

1.17 0.67 0.60 1.01 0.55

0.67 0.55 0.33 0.43

1.26

Upper

0 0 0 0

0 0 0 0 0

1 0 0 0

2

Nfs

Dear 2013 Drozd 2013

Dear 2013 Cooper 2011 Dear 2013 Dear 2013

Dear 2013 Dear 2013 Cooper 2011 Drozd 2013 Cooper 2011

Cooper 2011

Lead author

Abbreviations: d: Cohen’s effect size; CI: 95% confidence interval; T vs C: treatment vs. wait list or usual care; Treatment: single group (treatment only); Completers: d calculated on treatment completers; ITT: d calculated on intention-to-treat data.

Self-efficacy Life satisfaction

Pain cognitions

Anxiety

Quality of life

Treatment T vs C Treatment Treatment

Treatment Treatment T vs C T vs C T vs C

Wisconsin Brief Pain Questionnaire Roland Morris Disability Questionnaire Short Form-36 mental component Positive and Negative Affect Schedule Multiple Sclerosis Impact Scale -29 item - Physical - Psychological Generalised Anxiety Disorder-7 item

Affect

T vs C

Short Form-36 physical functioning

Physical function & disability

Design

Measure

Construct

Table 3. Longer-term treatment effects for standardised measures administered at follow-up.

198 Journal of Telemedicine and Telecare 21(4)

Charova et al. depression is considered clinically meaningful) which are also likely to vary according to the characteristics of a chronic illness group,1,2 may influence the sensitivity and specificity of a depression measure resulting in over- or underestimation of treatment effects.62,63 Finally, the current findings may be undermined by publication bias, with a tendency for academic journals to publish significant findings.64 The calculated fail safe Ns acknowledge this issue but can also underestimate the extent of bias.65

199

7.

8.

9.

Conclusions This is the first review to systematically and quantitatively examine the impact of targeted web-based interventions on depression outcomes in populations with chronic illness. The initial indication of moderate to large reductions in depression symptomatology is promising. Further controlled research is required to confirm web interventions as an evidence-based practice, including optimal treatment format, frequency and duration.

10.

11. 12.

13.

Acknowledgements The authors would like to acknowledge the assistance of Research Librarian M. Bell, University of Adelaide, with the database searches. Thanks must also go to the authors of included studies for providing further details on request.

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15.

Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

16.

Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: EC is supported by a Nursing and Allied Health Scholarship and Support Scheme Clinical Psychology Scholarship. PJT is supported by the National Health and Medical Research Council of Australia (Neil Hamilton Fairley —Clinical Overseas Fellowship #1053578).

References *References marked with an asterisk indicate studies included in the review. 1. Clarke DM, Currie KC. Depression, anxiety and their relationship with chronic diseases: a review of the epidemiology, risk and treatment evidence. Med J Aust 2009;190(7): S54–S60. 2. Ha¨rter M, Baumeister H, Reuter K, et al. Increased 12-month prevalence rates of mental disorders in patients with chronic somatic diseases. Psychother Psychosom 2007;76:354–360. 3. Berger M, Wagner TH, Baker LC. Internet use and stigmatized illness. Soc Sci Med 2005;61:1821–1827. 4. Rizzo M, Creed F, Goldberg D, et al. A systematic review of non-pharmacological treatments for depression in people with chronic physical health problems. J Psychosom Res 2011;71:18–27. 5. Campos B. Telepsychology and telehealth: Counselling conducted in a technology environment. Counselling, Psychotherapy, and Health 2009;5:26–59. 6. Simblett SK, Ring H, Bateman A. Computerised cognitive behavioural therapy (cCBT): A possible treatment for mood

17.

18.

19.

20.

21.

22.

23. 24.

25.

disorders experienced by people with neurological conditions? Neuropsychol Rehabil 2011;21(6): 925–928. 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. Andrews G, Cuijpers P, Craske MG, et al. Computer therapy for the anxiety and depressive disorders is effective, acceptable and practical health care: a meta-analysis. PLoS One 2010;5(10): 1–6. Barak A, Hen L, Boniel-Nissim M, et al. A comprehensive review and a meta-analysis of the effectiveness of internetbased psychotherapeutic interventions. J Technol Hum Serv 2008;26(2–4): 109–160. Hedman E, Ljotsson B, Lindefors N. Cognitive behavior therapy via the Internet: a systematic review of applications, clinical efficacy and cost-effectiveness. Expert Rev Pharmacoecon Outcomes Res 2012;12(6): 745–764. Johansson R, Andersson G. Internet-based psychological treatments for depression. Expert Rev Neurother 2012;12(7): 861. Spek V, Cuijpers P, Nyklicek I, et al. Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychol Med 2007;37(3): 319–328. Richards D, Richardson T. Computer-based psychological treatments for depression: A systematic review and metaanalysis. Clin Psychol Rev 2013;32:329–342. Bashshur RL, Shannon GW, Smith BR, et al. The empirical foundations of telemedicine interventions for chronic disease management. Telemed J E Health 2014;20(9): 771–800. Beatty L, Lambert S. A systematic review of internet-based self-help therapeutic interventions to improve distress and disease-control among adults with chronic health conditions. Clin Psychol Rev 2013;33:609–622. Paul CL, Carey ML, Sanson-Fisher RW, Houlcroft LE, Turon HE. The impact of web-based approaches on psychosocial health in chronic physical and mental health conditions. Health Educ Res 2013;28(3): 450–471. Laakso E, Armstrong K, Usher W. Cyber-management of people with chronic disease: A potential solution to eHealth challenges. J Health Educ 2012;71(4): 483–490. Murray E, Burns J, See Tai S, et al. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev 2005;4: DOI: 10.1002/ 14651858.CD004274.pub4 *Duffecy J, Sanford S, Wagner L, et al. Project onward: an innovative e-health intervention for cancer survivors. Psychooncology 2013;22:947–951. Edwards L, Thomas C, Gregory A, et al. Are people With chronic diseases interested in using telehealth? A cross-sectional postal survey. J Med Internet Res 2014;16(5): e123. American Psychiatric Association. The Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text rev. Washington, DC: Author, 2000. World Health Organization. International statistical classification of diseases and related health problems, 10th ed., revision. Geneva: WHO Publications, 2010. Barak A, Klein B, Proudfoot JG. Defining internet-supported therapeutic interventions. Ann Behav Med 2009;38:4–17. Schutz LE, Rivers KO, Ratusnik DL. The role of external validity in evidence-based practice for rehabilitation. Rehabil Psychol 2008;53(3): 294. Higgins JPT and Green S (editors) (2008) Cochrane handbook for systematic reviews of interventions. Available at: http://www.cochrane.org/handbook.

200 26. *Cooper CL, Hind D, Parry GD, et al. Computerised cognitive behavioural therapy for the treatment of depression in people with multiple sclerosis: external pilot trial. Trials 2011;12:259–273. 27. *Drozd F, Skeie LG, Kraft P, et al. A web-based intervention trial for depressive symptoms and subjective well-being in patients with chronic HIV infection. AIDS Care 2013;26(9): 1080–1089. 28. *Boeschoten RE, Nieuwenhuis MM, van Oppen P, et al. Feasibility and outcome of a web-based self-help intervention for depressive symptoms in patients with multiple sclerosis: A pilot study. J Neurol Sci 2012;315:104–109. 29. *Bond GE, Burr RL, Wolf FM, et al. The effects of a webbased intervention on psychosocial well-being among adults aged 60 and older with diabetes: A randomised trial. Diabetes Educ 2010;36(3): 446–456. 30. *Bundy C, Pinder B, Bucci S, et al. A novel, web-based, psychological intervention for people with psoriasis: the electronic targeted intervention for psoriasis (eTIPS) study. Br J Dermatol 2014;169:329–336. 31. *Cohn MA, Pietrucha ME, Saslow LR, et al. An online positive affect skills intervention reduces depression in adults with type 2 diabetes. J Posit Psychol 2014;9(6): 523–534. 32. *Dear BF, Titov N, Perry KN, et al. The Pain Course: A randomised controlled trial of a clinician-guided Internetdelivered cognitive behaviour therapy program for managing chronic pain and emotional well-being. Pain 2013;154:942–950. 33. *Glozier N, Christensen H, Naismith S, et al. Internet-delivered cognitive behavioural therapy for adults with mild to moderate depression and high cardiovascular disease risks: A randomised attention-controlled trial. Plos One 2013;8(3): e59139. 34. *Thompson NJ, Walker ER, Obolensky N, et al. Distance delivery of mindfulness-based cognitive therapy for depression: Project UPLIFT. Epilepsy Behav 2010;19:247–254. 35. *van Bastelaar K, Pouwer F, Cuijpers P, et al. Web-Based depression treatment for Type 1 and Type 2 diabetic patients. Diabetes Care 2011;34:320–325. 36. Moher D, Liberati A, Tetzlaff J, et al. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 2009;6(7): e1000097. 37. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health 1998;52:377–384. 38. Stemler SE. A Comparison of consensus, consistency, and measurement approaches to estimating interrater reliability. Practical Assessment, Research and Evaluation 2004;9(4): 1–19. 39. Cohen J. A power primer. Psychol Bull 1992;112(1): 155–159. 40. Morris SB, DeShon RP. Combining effect size estimates in meta-analysis with repeated measures and independentgroups designs. Psychol Methods 2002;7:105–125. 41. Gupta SK. Intention-to-treat concept: a review. Perspect Clin Res 2011;2:109–112. 42. Lipsey MW, Wilson DB. Practical meta-analysis. London: Sage, 2001. 43. Higgins JPT, Thompson SG, Deeks JJ, Altman DG. Measuring inconsistency in meta-analyses. BMJ 2003;327:557–560.

Journal of Telemedicine and Telecare 21(4) 44. Orwin RG. A fail-safe N for effect size in meta-analysis. J Educ Behav Stat 1983;8(2): 157–159. 45. Misra S. Randomised double blind placebo control studies, the ‘‘Gold Standard’’ in intervention based studies. Indian J Sex Transm Dis 2012;33(2): 131–134. 46. Andersen RM. Revisiting the behavioral model and access to medical care: Does it matter? J Health Soc Behav 1995;36:1–10. 47. Bane C, Hughes CM, McElnay JC. The impact of depressive symptoms and psychosocial factors on medication adherence in cardiovascular disease. Patient Educ Couns 2006;60:187–193. 48. Bruce ML. Psychosocial risk factors for depressive disorders in late life. Bio Psychiatry 2002;52:175–184. 49. Abbott JM, Klein B, Ciechomski L. Best practices in online therapy. J Technol Human Serv 2008;26(2-4): 360–375. 50. Lauder S, Chester A, Berk M. Net-effect? Online psychological interventions. Acta Neuropsychiatr 2007;19: 386–388. 51. Titov N, Andrews G, Davies M, et al. Internet treatment for depression: A randomised controlled trial comparing clinician vs. technician assistance. Plos One 2010;5(6): e10939. 52. Topolovec-Vranic J, Cullen N, Michalak A, et al. Evaluation of an online cognitive behavioural therapy program by patients with traumatic brain injury and depression. Brain Inj 2010;24(5): 762–772. 53. Kaltenthaler E, Sutcliffe P, Parry G, et al. The acceptability to patients of computerized cognitive behaviour therapy for depression: a systematic review. Psychol Med 2008;38:1521–1530. 54. Brouwer B, Oenema A, Raat H, et al. Characteristics of visitors and revisitors to an Internet-delivered computer-tailored lifestyle intervention implemented for use by the general public. Health Educ Res 2014;25(4): 585–595. 55. Perdices M, Tate RL. Single-subject designs as a tool for evidence based clinical practice: Are they unrecognised and undervalued? Neuropsychol Rehabil 2009;19:904–927. 56. Bonell CP, Hargreaves J, Cousens S, et al. Alternatives to randomisation in the evaluation of public health interventions: design challenges and solutions. J Epidemiol Community Health 2011;65(7): 582–7. 57. Donkin L, Christensen H, Naismith SL, et al. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res 2011;13(3): e52. 58. Chapman DP, Perry GS, Strine TW. The vital link between chronic disease and depressive disorders. Prev Chronic Dis 2005;2(1): 1–10. 59. Cella D, Riley W, Stone A, et al. The Patient-Reported Outcomes Measurement Information System (PROMIS) developed and tested its first wave of adult self-reported health outcome item banks: 2005-2008. J Clinical Epidemiol 2010;63:1179–1194. 60. Choi SW, Schalet B, Cook KF, et al. Establishing a common metric for depressive symptoms: Linking the BDI-II, CESD, and PHQ9 to PROMIS depression. Psychol Assess 2014;26(2): 513–527. 61. Faraone SV. Interpreting estimates of treatment effects: Implications for managed care. P & T 2008;33(12): 700–703, 710-711. 62. Sheehan AM, McGee H. Screening for depression in medical research: ethical challenges and recommendations. BMC Medical Ethics 2013;14:4.

Charova et al. 63. Moussavi S, Chatterji S, Verdes E, et al. Depression, chronic diseases, and decrements on health: Results from the World Health Surveys. Lancet 2007;370(8): 851–858. 64. Easterbrook PJ, Berlin JA, Goplan R, et al. Publication bias in clinical research. Lancet 1991;337:867–872.

201 65. Levine T, Asada KJ, Carpenter C. Sample sizes and effect sizes negatively correlated in meta-analyses: Evidence and implications of a publication bias against non-significant findings. Commun Monogr 2009;76(3): 286–302.

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