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

Abstract

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 = 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 (d_w = 0.36, CI = 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 = 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

ehealth online health self care telehealth

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 longer-term biopsychosocial effects of web-based interventions; 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 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

Figure 1.

Flowchart of article selection.

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 studies^26,27 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 = 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 = 0.87, p < 0.01³⁸). Consensus quality scores were subsequently achieved through discussion (see online supplementary material).

Effect size estimation

Cohen’s d effect sizes^39,40 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, intention-to-treat (ITT) data were utilised as recommended for 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 (N_fs).⁴² The Nfs statistic estimates the number of unpublished studies with non-significant results (d = 0.20)⁴⁴ which would be required to exist in order to overturn the findings. Larger N_fs 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 = 10, range = 20 to 91 years), with the majority of participants being female (67%, n = 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 = 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.

Table 1.

Summary of included studies (N = 11 studies).

Lead author (year)	Country	Sample characteristics				Methodology
Lead author (year)	Country	Diagnosis	Depression criteria	N	Mean age (SD)	Years since diagnosis (SD/range)	Depression outcome	Control	Follow up	Intervention details
Boeschoten (2012)	Netherlands	Multiple Sclerosis	BDI-II ≥ 16	44	45 (12)	5 (2-40)	BDI-II	Nil	Nil	• ‘Allesondercontrole’ • 5 weeks/ 5 sessions • Weekly e-mails • Trainee psychologists • Problem Solving Therapy
Bond (2010)	United States	Diabetes	Nil	62	67 (6)	Control: 17.8 (11.7) Treatment: 16.1 (10.5)	CES-D	Usual care	Nil	• 6 months/ weekly • Instant messaging, chat, e-mail, bulletin board • Psychologist, Clinical nurse, peers
Bundy (2013)	United Kingdom	Psoriasis	Nil	135	45 (13)	14 (0-30)	HADS	Waitlist	6 months	• eTIPs • 6 weeks/ 6 sessions/ weekly • Therapy modules plus education • CBT
Cohn (2014)	United States	Diabetes	Nil	53	54 (-)	–	CES-D	Waitlist	Nil	• ‘DAHLIA’ • 8 weeks/ 5 sessions/ weekly • Stress and Coping Theory/ Broaden-and- Build Theory
Cooper (2011)	United Kingdom	Multiple Sclerosis	BDI-II ≥ 14	24	45 (8)	–	BDI-II; MSIS-29	Usual care	13 weeks	• ‘Beating the Blues’ • 8 weeks/ 8 sessions (50min)/ weekly • CBT
Dear (2013)	Australia	Chronic pain	Nil	62	49 (13)	7.4 (8.1)	PHQ9	Waitlist	3 months	• ‘PainCourse’ • 8 weeks/ 5 sessions/ weekly • Weekly telephone calls, e-mail • Psychologist • CBT
Drozd (2013)	Norway	HIV	None	67	48 (9)	Control: 11.1 (6.8) Treatment: 11.6 (6.6)	CES-D	Usual care	3 months	• ‘Avanti’ • 1 month/ 14 sessions • Meta-cognitive/ Positive Psychology
Duffecy (2013)	United States	Cancer	HADS ≥ 8	31	50 (-)	–	HADS	Online health education and monitoring	Nil	• ‘Mood-Manager’ • 8 weeks/ 16 sessions (10-15min)/ biweekly • Modules, peer discussion board • CBT
Glozier (2013)	Australia	CVD	K-10 ≥ 16	562	58 (7)	–	PHQ9	Online health education	Nil	• ‘E-couch’ • 12 weeks/ 12 sessions (30-60min)/ weekly • Phone call, email, text messaging • CBT/Interpersonal psychotherapy
Thompson (2010)	United States	Epilepsy	CES-D > 13	53	36 (-)	–	mBDI	• Usual care • Phone intervention	8 weeks	• ‘UPLIFT’ • 8 weeks/ 8 sessions (60min)/ weekly • Video instruction, skill building, discussion board, homework • Psychologist, peer • CBT
van Bastelaar (2011)	Netherlands	Diabetes	CES-D ≥ 16	255	50 (12)	Type 1: 21 (13) Type II: 9 (8)	CES-D	Waitlist	1 month	• ‘Coping with Depression’ • 8 sessions • Modules, standardised email feedback • CBT

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.

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 = 4, range = 5 to 16) delivered on a weekly or biweekly basis over 1 to 6 months (median = 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 = 19, SD = 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 studies^{26,27,28,33,35} 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 = 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 d_w (Figure 2). A more conservative treatment effect was noted when randomised controlled trials were examined only (d_w = 0.31; CI = 0.17 to 0.45; p < 0.01). Although appropriate heterogeneity was demonstrated (I²= 19.73%, CI = 0.00–71.74%), the associated CI suggests a wide spectrum of possible degrees of heterogeneity (i.e. mild to substantial).

Figure 2.

Forest plot of short-term depression outcomes.

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.

Table 2.

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

Construct	Outcome measure	Design	N _studies	N _participants	Analysis	d	95% CI		Nfs	Lead author
Construct	Outcome measure	Design	N _studies	N _participants	Analysis	d	Lower	Upper	Nfs	Lead author
Social support	Diabetes Support Scale	T vs C	1	62	–	1.10*	0.58	1.62	5	Bond 2010
Self-efficacy	Pain Self-efficacy Questionnaire	T vs C	1	62	ITT	1.05*	0.55	1.57	4	Dear 2013
	Diabetes Empowerment Scale	T vs C	1	62	–	0.68*	0.18	1.18	2	Bond 2010
Quality of life	Multiple Sclerosis Impact Scale – 29 item	T vs C	1	23	Completers					Cooper 2011
	- Psychological					0.81	−0.03	1.65	3
	- Physical					0.17	−0.64	0.98	0
	Problem Areas in Diabetes Scale	T vs C	1	62	–	0.45	−0.04	0.94	1	Bond 2010
	Dermatology Life Quality Index	T vs C	1	76	Completers	−0.44	0.00	−0.88	1	Bundy 2013
	EuroQol 5 item	Treatment	1	44	ITT	0.10	−0.20	0.40	0	Boeschoten 2012
Problem solving	Social Problem Solving Inventory Revised	Treatment	1	44	ITT					Boeschoten 2012
	- Negative Problem Orientation					0.44*	0.09	0.79	1
	- Avoidance					0.18	−0.11	0.47	1
	- Positive Problem Orientation					0.07	−0.23	0.37	0
	- Impulsivity/Carelessness					0.04	−0.26	0.34	0
	- Rational Problem Solving					0.02	−0.28	0.32	0
Physical function & disability	Wisconsin Brief Pain Questionnaire	T vs C	1	62	ITT	0.68*	0.18	1.18	2	Dear 2013
	Roland Morris Disability Questionnaire	T vs C	1	62	ITT	0.59*	0.10	1.08	2	Dear 2013
	Short Form 36 - physical functioning	T vs C	1	22	Completers	−0.13	−0.96	0.70	0	Cooper 2011
		Treatment	1	44	ITT	0.00	−0.30	0.30	0	Boeschoten 2012
	Psoriasis Area and Severity Index	T vs C	1	85	Completers	0.06	−0.38	0.50	0	Bundy 2013
	WHODAS II	T vs T	1	562	ITT	0.05	−0.15	0.25	0	Glozier 2013
Anxiety	Generalised Anxiety Disorder – 7 item	T vs C	1	62	ITT	0.61*	0.12	1.10	2	Dear 2013
		T vs C	1	23	Completers	0.27	−0.54	1.08	0	Cooper 2011
		T vs T	1	562	ITT	0.23*	0.03	0.43	0	Glozier 2013
	Hospital Anxiety and Depression Scale- anxiety	T vs C	1	78	Completers	0.36	−0.08	0.80	1	Bundy 2013
		Treatment	1	44	ITT	0.28	−0.02	0.58	0	Boeschoten 2012
Pain cognitions	Pain Responses Self-Statements	T vs C	1	62	ITT					Dear 2013
	- Catastrophising	T vs C				0.54*	0.05	1.03	2
	- Coping					0.54*	0.05	1.03	2
	TAMPA Scale of Kinesiophobia	T vs C	1	62	ITT	0.53*	0.05	1.01	2	Dear 2013
Affect	Short Form 36 – mental component	T vs C	1	22	Completers	0.49	−0.36	1.34	1	Cooper 2011
	Positive and Negative Affect Schedule	T vs C	1	67	ITT	0.30	−0.18	0.78	1	Drozd 2013
	Differential Emotions Scale	T vs C	1	42	Completers					Cohn 2014
	- negative affect					0.03	−0.59	0.65	0
	- positive affect					0.02	−0.60	0.64	0
Stress	Perceived Stress Scale	T vs C	1	42	Completers	0.16	−0.46	0.78	0	Cohn 2014
Compliance	Medical Outcomes Study - patient adherence	T vs T	1	562	ITT	−0.27	−0.47	−0.07	0	Glozier 2013
Life satisfaction	Satisfaction with Life Scale	T vs C	1	67	ITT	0.07	−0.41	0.55	0	Drozd 2013

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 ≠ 0.

Four controlled studies provided 1 to 3 month follow-up 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 = 0.29, CI = 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 = 13). Additional psychosocial outcomes were not maintained following the cessation of treatment (Table 3).

Figure 3.

Forest plot of follow-up depression outcomes.

Table 3.

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

Construct	Measure	Design	N _studies	N _participants	Analysis	Follow-up	d	95% CI		Nfs	Lead author
Construct	Measure	Design	N _studies	N _participants	Analysis	Follow-up	d	Lower	Upper	Nfs	Lead author
Physical function & disability	Short Form-36 physical functioning	T vs C	1	19	Completers	13 weeks	0.61	−0.04	1.26	2	Cooper 2011
	Wisconsin Brief Pain Questionnaire	Treatment	1	31	ITT	3 months	0.32	−0.03	0.67	1	Dear 2013
	Roland Morris Disability Questionnaire	Treatment	1	31	ITT	3 months	0.20	−0.15	0.55	0	Dear 2013
Affect	Short Form-36 mental component	T vs C	1	19	Completers	13 weeks	−0.30	−0.93	0.33	0	Cooper 2011
	Positive and Negative Affect Schedule	T vs C	1	67	ITT	3 months	−0.05	−0.53	0.43	0	Drozd 2013
Quality of life	Multiple Sclerosis Impact Scale -29 item	T vs C	1	20	Completers	13 weeks					Cooper 2011
	- Physical						0.29	−0.59	1.17	0
	- Psychological						−0.21	−1.09	0.67	0
Anxiety	Generalised Anxiety Disorder-7 item	Treatment	1	31	ITT	3 months	0.26	−0.08	0.60	0	Dear 2013
		T vs C	1	21	Completers	13 weeks	0.15	−0.71	1.01	0	Cooper 2011
Pain cognitions	TAMPA Scale of Kinesiophobia	Treatment	1	31	ITT	3 months	0.21	−0.13	0.55	0	Dear 2013
	Pain Responses Self-Statements	Treatment	1	31	ITT	3months					Dear 2013
	- Coping						0.20	−0.14	0.54	0
	- Catastrophising						0.16	−0.18	0.50	0
Self-efficacy	Pain Self-efficacy Questionnaire	Treatment	1	31	ITT	3 months	0.17	−0.17	0.51	0	Dear 2013
Life satisfaction	Satisfaction with Life Scale	T vs C	1	67	ITT	3 months	0.02	−0.46	0.50	0	Drozd 2013

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.

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 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 web-based 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 self-management 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 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 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

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.

Footnotes

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.

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.

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

Clarke

Currie

. 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.

Härter

Baumeister

Reuter

. Increased 12-month prevalence rates of mental disorders in patients with chronic somatic diseases. Psychother Psychosom 2007; 76: 354–360.

Berger

Wagner

Baker

. Internet use and stigmatized illness. Soc Sci Med 2005; 61: 1821–1827.

Rizzo

Creed

Goldberg

. A systematic review of non-pharmacological treatments for depression in people with chronic physical health problems. J Psychosom Res 2011; 71: 18–27.

Campos

. Telepsychology and telehealth: Counselling conducted in a technology environment. Counselling, Psychotherapy, and Health 2009; 5: 26–59.

Simblett

Ring

Bateman

. Computerised cognitive behavioural therapy (cCBT): A possible treatment for mood disorders experienced by people with neurological conditions? Neuropsychol Rehabil 2011; 21(6): 925–928.

Andersson

Cuijpers

. Internet-based and other computerized psychological treatments for adult depression: a meta-analysis. Cogn Behav Ther 2009; 38(4): 196–205.

Andrews

Cuijpers

Craske

. 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

Hen

Boniel-Nissim

. A comprehensive review and a meta-analysis of the effectiveness of internet-based psychotherapeutic interventions. J Technol Hum Serv 2008; 26(2–4): 109–160.

10.

Hedman

Ljotsson

Lindefors

. 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.

11.

Johansson

Andersson

. Internet-based psychological treatments for depression. Expert Rev Neurother 2012; 12(7): 861–861.

12.

Spek

Cuijpers

Nyklicek

. Internet-based cognitive behaviour therapy for symptoms of depression and anxiety: a meta-analysis. Psychol Med 2007; 37(3): 319–328.

13.

Richards

Richardson

. Computer-based psychological treatments for depression: A systematic review and meta-analysis. Clin Psychol Rev 2013; 32: 329–342.

14.

Bashshur

Shannon

Smith

. The empirical foundations of telemedicine interventions for chronic disease management. Telemed J E Health 2014; 20(9): 771–800.

15.

Beatty

Lambert

. 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.

16.

Paul

Carey

Sanson-Fisher

Houlcroft

Turon

. The impact of web-based approaches on psychosocial health in chronic physical and mental health conditions. Health Educ Res 2013; 28(3): 450–471.

17.

Laakso

Armstrong

Usher

. Cyber-management of people with chronic disease: A potential solution to eHealth challenges. J Health Educ 2012; 71(4): 483–490.

18.

Murray

Burns

See Tai

. Interactive health communication applications for people with chronic disease. Cochrane Database Syst Rev 2005; 4. DOI: 10.1002/14651858.CD004274.pub4.

19.

*Duffecy

Sanford

Wagner

. Project onward: an innovative e-health intervention for cancer survivors. Psychooncology 2013; 22: 947–951.

20.

Edwards

Thomas

Gregory

. Are people With chronic diseases interested in using telehealth? A cross-sectional postal survey. J Med Internet Res 2014; 16(5): e123–e123.

21.

American Psychiatric Association. The Diagnostic and Statistical Manual of Mental Disorders, 4th ed., text rev. Washington, DC: Author, 2000.

22.

World Health Organization. International statistical classification of diseases and related health problems, 10th ed., revision. Geneva: WHO Publications, 2010.

23.

Barak

Klein

Proudfoot

. Defining internet-supported therapeutic interventions. Ann Behav Med 2009; 38: 4–17.

24.

Schutz

Rivers

Ratusnik

. The role of external validity in evidence-based practice for rehabilitation. Rehabil Psychol 2008; 53(3): 294–294.

25.

Higgins JPT and Green S (editors) (2008) Cochrane handbook for systematic reviews of interventions. Available at: http://www.cochrane.org/handbook.

26.

*Cooper

Hind

Parry

. Computerised cognitive behavioural therapy for the treatment of depression in people with multiple sclerosis: external pilot trial. Trials 2011; 12: 259–273.

27.

*Drozd

Skeie

Kraft

. 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

Nieuwenhuis

van Oppen

. 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

Burr

Wolf

. The effects of a web-based 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

Pinder

Bucci

. 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

Pietrucha

Saslow

. An online positive affect skills intervention reduces depression in adults with type 2 diabetes. J Posit Psychol 2014; 9(6): 523–534.

32.

*Dear

Titov

Perry

. The Pain Course: A randomised controlled trial of a clinician-guided Internet-delivered cognitive behaviour therapy program for managing chronic pain and emotional well-being. Pain 2013; 154: 942–950.

33.

*Glozier

Christensen

Naismith

. 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

Walker

Obolensky

. Distance delivery of mindfulness-based cognitive therapy for depression: Project UPLIFT. Epilepsy Behav 2010; 19: 247–254.

35.

*van Bastelaar

Pouwer

Cuijpers

. Web-Based depression treatment for Type 1 and Type 2 diabetic patients. Diabetes Care 2011; 34: 320–325.

36.

Moher

Liberati

Tetzlaff

. Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLoS Med 2009; 6(7): e1000097–e1000097.

37.

Downs

Black

. 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

. A Comparison of consensus, consistency, and measurement approaches to estimating interrater reliability. Practical Assessment, Research and Evaluation 2004; 9(4): 1–19.

39.

Cohen

. A power primer. Psychol Bull 1992; 112(1): 155–159.

40.

Morris

DeShon

. Combining effect size estimates in meta-analysis with repeated measures and independent-groups designs. Psychol Methods 2002; 7: 105–125.

41.

Gupta

. Intention-to-treat concept: a review. Perspect Clin Res 2011; 2: 109–112.

42.

Lipsey

Wilson

. Practical meta-analysis, London: Sage, 2001.

43.

Higgins

JPT

Thompson

Deeks

Altman

. Measuring inconsistency in meta-analyses. BMJ 2003; 327: 557–560.

44.

Orwin

. A fail-safe N for effect size in meta-analysis. J Educ Behav Stat 1983; 8(2): 157–159.

45.

Misra

. 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

. Revisiting the behavioral model and access to medical care: Does it matter? J Health Soc Behav 1995; 36: 1–10.

47.

Bane

Hughes

McElnay

. The impact of depressive symptoms and psychosocial factors on medication adherence in cardiovascular disease. Patient Educ Couns 2006; 60: 187–193.

48.

Bruce

. Psychosocial risk factors for depressive disorders in late life. Bio Psychiatry 2002; 52: 175–184.

49.

Abbott

Klein

Ciechomski

. Best practices in online therapy. J Technol Human Serv 2008; 26(2-4): 360–375.

50.

Lauder

Chester

Berk

. Net-effect? Online psychological interventions. Acta Neuropsychiatr 2007; 19: 386–388.

51.

Titov

Andrews

Davies

. Internet treatment for depression: A randomised controlled trial comparing clinician vs. technician assistance. Plos One 2010; 5(6): e10939–e10939.

52.

Topolovec-Vranic

Cullen

Michalak

. 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

Sutcliffe

Parry

. The acceptability to patients of computerized cognitive behaviour therapy for depression: a systematic review. Psychol Med 2008; 38: 1521–1530.

54.

Brouwer

Oenema

Raat

. 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

Tate

. Single-subject designs as a tool for evidence based clinical practice: Are they unrecognised and undervalued? Neuropsychol Rehabil 2009; 19: 904–927.

56.

Bonell

Hargreaves

Cousens

. 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

Christensen

Naismith

. A systematic review of the impact of adherence on the effectiveness of e-therapies. J Med Internet Res 2011; 13(3): e52–e52.

58.

Chapman

Perry

Strine

. The vital link between chronic disease and depressive disorders. Prev Chronic Dis 2005; 2(1): 1–10.

59.

Cella

Riley

Stone

. 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

Schalet

Cook

. Establishing a common metric for depressive symptoms: Linking the BDI-II, CES-D, and PHQ9 to PROMIS depression. Psychol Assess 2014; 26(2): 513–527.

61.

Faraone

. Interpreting estimates of treatment effects: Implications for managed care. P & T 2008; 33(12): 700–703, 710-711.

62.

Sheehan

McGee

. Screening for depression in medical research: ethical challenges and recommendations. BMC Medical Ethics 2013; 14: 4–4.

63.

Moussavi

Chatterji

Verdes

. Depression, chronic diseases, and decrements on health: Results from the World Health Surveys. Lancet 2007; 370(8): 851–858.

64.

Easterbrook

Berlin

Goplan

. Publication bias in clinical research. Lancet 1991; 337: 867–872.

65.

Levine

Asada

Carpenter

. 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.