Effectiveness of eHealth mindfulness-based interventions on cancer-related symptoms among cancer patients and survivors: A systematic review and meta-analysis

Abstract

Objectives

This review aimed to synthesize the best available evidence concerning the effectiveness of electronic health, mindfulness-based interventions (eHealth-MBIs) on anxiety, depression, mindfulness and quality of life (QoL) among cancer patients/survivors.

Methods

Published and unpublished studies were retrieved from 10 electronic databases. Two independent authors screened and selected articles, extracted data using a standardized form and appraised the studies with the Cochrane risk of bias assessment tool. Meta-analyses were performed using a random-effect model with Review Manager (REVMAN). Standardized mean differences (SMDs) were used to determine intervention effects. Narrative syntheses were conducted for studies not suitable for meta-analyses. Heterogeneity was identified through I² and chi-square statistics. Subgroup analyses were performed based on types of controls, age groups and gender. The Grading of Recommendations Assessment, Development and Evaluation approach was used to assess overall quality.

Results

In total, 18 studies were included. The eHealth-MBIs improved primary outcomes of anxiety (SMD = −0.28) and depression (SMD = −0.24), among cancer patients/survivors with small effect sizes. Effects for the secondary outcomes of QoL (SMD = 0.25) and mindfulness (SMD = 0.29) were observed at short-term follow-up assessments but not at post-intervention. Overall quality was rated as low for the primary and very low for the secondary outcomes.

Conclusion

The eHealth-MBIs can be offered as a cost-effective and accessible alternative for cancer patients and survivors in healthcare settings. Future research may further explore the effectiveness of eHealth-MBIs based on different types of MBIs, cancer types, modes of delivery and other outcomes such as stress and post-traumatic growth.

Keywords

Anxiety cancer depression electronic health meta-analysis mindfulness intervention quality of life,telehealth

Introduction

Cancer is a leading cause of morbidity and mortality worldwide, with individuals having 21.4% risk of developing and 17.7% chance of dying from cancer.¹ Cancer involves uncontrolled and abnormal cell growth, spreading through circulatory and lymphatic systems.² Within a month of the cancer diagnosis, many cancer patients express symptoms such as anxiety, depression, pain and dyspnoea.³ Similarly, cancer survivors, referring to those surviving three to five years from diagnosis without recurrence, experience prolonged physical and psychosocial morbidities.^4,5

Cancer is associated with anxiety, depression and lower quality of life (QoL). Anxiety manifests as persistent and uncontrollable excessive worry and physical symptoms (such as restlessness and muscle tension).⁶ Depression is identified by persistently anhedonia, low mood, physical symptoms (such as fatigue and sleep disturbances) and recurrent suicidal thoughts/attempts.⁶ QoL reflects individuals’ sense of well-being and capability in performing activities of daily living.² Among cancer patients, 82.3% of them reported below-average QoL due to cancer-related psychological and physical symptoms.⁷

Mindfulness refers to being attentive on purpose, in the present-moment and being non-judgemental.⁸ One of mindfulness mechanisms is ‘decentering’, where one observes experiences through a third-person perspective.⁹ It is postulated that mindfulness promotes acceptance, restructures thoughts and enhanced positive emotions.^10–12 Biologically, mindfulness interventions increase brain activity in the insula and anterior cingulate cortex regions, which regulate the present-moment awareness and non-judgemental acceptance process, respectively.¹³ Mindfulness training promotes withdrawal from stressful situations, reduces the activation of HPA axis and improves physical and mental health.^14,15

Mindfulness-based interventions (MBIs) are effective complementary therapy in managing cancer-related symptoms.¹⁶ Most MBIs are based on an eight-week mindfulness-based stress reduction (MBSR) program, which manages chronic pain through weekly guided group-based meditation, daily audio-guided home practice meditation and mindfulness retreat.^17,18 Mindful attention towards body sensations is promoted through practices such as body scans and sitting mediation.¹⁷ Cognitive behavioural therapy has been combined with MBSR, resulting in mindfulness-based cognitive therapy (MBCT) for preventing depression relapses.¹⁹ MBIs were later adapted for other populations and outcomes.²⁰ Mindfulness-based cancer recovery (MBCR), an adaptation of MBSR, comprises contents tailored for cancer patients.²¹ Furthermore, acceptance and commitment therapy (ACT) addresses psychological issues by increasing psychological flexibility (the ability to perform mindful and value-guided actions).^22,23

Electronic health (eHealth) refers to health services delivered through information and communication technologies such as telephone, mobile applications, tablet and computers.²⁴ Utilizing eHealth-MBIs improves affordability, accessibility, cost-effectiveness and quality care without the need for in-person sessions.²⁵ The eHealth is especially valuable in the midst of the COVID-19 pandemic, where cancer patients/survivors may have increased anxiety and concerns over the accessibility of healthcare resources, social isolation and the risk of COVID-19 infection.²⁶

Most existing systematic reviews (SRs) reported the effects of in-person cancer patients/survivors.^16,27–31 Only one SR³² examined the effects of eHealth-MBIs on psychological and somatic outcomes for cancer patients. However, this SR searched only four databases, which might elevate the risk of publication bias, threatening validity of research findings.³³ Furthermore, SR did not perform meta-analysis, subgroup analysis and sensitivity analysis. As such, the variations between individual studies and pooled analysis were not produced,³⁴ leading to inconclusive findings. Our SR attempted to minimize such gaps in the literature. We aimed to systematically synthesize the effectiveness of eHealth-MBIs on anxiety, depression, QoL and mindfulness on cancer patients/survivors using comprehensive search. The eHealth is a cost-effective solution in the context of increasing healthcare expenditure globally.³⁵

Methods

This SR followed the Cochrane Handbook for Systematic Reviews of Interventions.³⁶ The protocol has been registered in PROSPERO database (CRD42020209870).

Eligibility criteria

Given limited randomized controlled trials (RCTs) in this domain, this SR considered studies utilizing RCTs and quasi-experimental research design. Only studies conducted between the years 2005 and 2020 and in English were included. Concerning participants, we considered studies involving cancer patients/survivors aged 18 years and above. Cancer patients referred to those diagnosed by a physician and cancer survivors are defined as those surviving beyond three to five years after the cancer diagnosis without any recurrence.⁵ Regarding the interventions, we considered studies delivering eHealth-MBIs and compared them with any type of controls (such as active treatment, waitlist or standard care). Primary outcomes comprised anxiety and depression and secondary outcomes were QoL and mindfulness.

Search strategy

A comprehensive search was performed on 10 electronic databases. Published articles were retrieved from PubMed, Embase, The Cochrane Library, CINAHL, PsycINFO, MEDLINE and the Web of Science. Unpublished articles were retrieved from Google scholar, Clinical Trials Registry and ProQuest Dissertation and Theses. Terms like ‘cancer’, ‘mindfulness-based intervention’ and ‘electronic health’ were used to build Medical Subject Heading (MeSH) terms and corresponding keywords. These keywords were combined with Boolean operators, ‘AND’ and ‘OR’. Additional studies were retrieved by handsearching and scanning references of similar SRs. Data for relevant ongoing trials were requested from authors by email. Search strategies are listed in Appendix A (Supplementary information). All search results were exported to EndNote X9 and duplicates were removed. Two independent reviewers selected potential records by screening abstracts and titles; and relevant records were further assessed by using their as full-text files. Discrepancies between authors were reconciled through discussions or by consulting a third reviewer.

Data extraction

Data were independently extracted using a data extraction form adapted from the Cochrane Handbook.³⁶ Disagreements between the two authors were resolved through discussions and/or consultation with the third reviewer. For studies with companion publications, the study with the most complete dataset associated with the outcomes of this review took precedence. Pilot studies that had a full study published were excluded to ensure more conclusive results.

Risk of bias assessment

Risk of bias was independently assessed by two reviewers using the Cochrane risk-of-bias tool, with differences reconciled through discussion.³⁶ Six domains encompassed random sequence generation, allocation concealment, blinding of participants/personnel, blinding of outcome assessment, incomplete outcome data and selective outcome reporting. Each domain was judged as low, high or unclear risk. Furthermore, the overall quality of evidence was rated using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach.³⁷

Data synthesis

We performed meta-analysis to synthesize the extracted data using review manager software (RevMan) with an inverse-variance method. Intervention effects would be represented by standardized mean difference (SMD) with 95% confidence interval (CI).³⁶ The value of SMD less than 0.5 would be interpreted as small, SMD ≥ 0.5 as medium and SMD ≥ 0.8 as large effect size.³⁸ As this review sought to generalize the conclusions, and true homogeneity among studies cannot be assumed to exist, meta-analysis was conducted using a random-effect model.³⁹ As recommended by the Cochrane Handbook, results should not focus only on statistical significance or non-significance using p-value to prevent conclusion deriving from insufficient evidence.³⁶ Authors of studies with missing data were contacted through email. However, if no data were provided, a narrative synthesis would be conducted. Heterogeneity of effect sizes was determined with significant chi-square (p < 0.10) and I² statistic more than 60%.³⁶ Sensitivity analysis was conducted by comparing I² of meta-analysis when all studies were pooled together and after the removal of each study. Additionally, subgroup analyses were conducted to examine effect sizes across different subgroups.

Results

Search results are portrayed in the PRISMA diagram (Figure 1)⁴⁰ and a total of 2687 records were retrieved. Subsequently, 294 full-text articles were assessed for eligibility and 18 studies were selected. Thirteen of which were RCTs and five were single-group quasi-experimental research (Table 1). Participants’ average ages ranged from 26.9 to 71.2 years. Six studies had participants with breast cancer, five with mixed cancer types, two with lung cancer and five with other cancer types. Four studies were based on MBSR, two on MBCT, two on ACT and one on MBCR (Appendix B; Supplementary information). Delivery methods encompassed tablet (n = 3), smartphone/telephone (n = 5), audio-CD (n = 2), computer (n = 2), MP3 player (n = 1), online videos (n = 1) and combined technologies (n = 3).

Figure 1.

The PRISMA diagram.

Table 1.

Characteristics of included studies.

Author (year)Country	Cancer type	Mean age(SD/range)Gender(n%)	Sample size	Intervention	Comparator	Attrition rate	ITT	Published protocol	Grant
Altschuler et al., 2012⁴¹ USA (California)	Mixed	56 (31-70) Male: 9% Female: 91%	Total: 23	CD-based MBSR	N.A. (Single group)	15%	No	No	Yes
Atreya et al., 2018⁴² USA (California)	Mixed	52 (23-78) Male: 36.4% Female: 63.6%	Total: 33	Audio-based mindfulness meditation program	N.A. (Single group)	24.24%	Yes	Yes	Yes
Campo et al., 2017⁴³ USA (North Carolina)	Mixed	26.9 (2.12) Female only	Total: 25	Mindful self-compassion videoconference	N.A. (Single group)	8%	No	No	Yes
Chambers et al., 2017⁴⁴ Australia, New Zealand	Advanced prostate cancer	I: 70.19 (8.71) C: 71.18 (8.43) Male only	Total: 189 I: 94 C: 95	Telephone-based MBCT	Usual care minimally enhanced with patient education	Total: 7.94% I: 15.96% C: 0%	Yes	No	Yes
Grewal et al., 2019⁴⁵ USA (Chicago)	Breast cancer	NR Female only	Total: 30	Mindfulness app (Headspcae)	N.A. (Single group)	20%	NR	No	NR
Hathiramani et al., 2020⁴⁶ UK (London)	Lymphoma	Total: 61 (16.7) I: 60.4 (19.4) C: 61.5 (13.9) Male: 37% Female: 63%	Total: 46 I: 23 C: 23	CD-based Mindfulness Relaxation	Exercise intervention	0%	No	Yes	No
Huberty et al., 2019⁴⁷ USA	Myeloproliferative neoplasm	Total: 58 (12) Male: 81.25% Female: 18.75%	Total: 128 Breakdown of the number in each arm is unavailable due to study design's limitations	Mindfulness app 1) 10% Happier app 2) Calm app	Educational handout	26.6% Unable to get attrition rate for each arm due to study design's limitations	No	No	Yes
Kubo et al., 2020⁴⁸ USA (Northern California)	Mixed	I: 65.8 (8.8) C: 67.1 (10.4) Male: 30.1% Female: 69.9%	Total: 103 I: 52 C: 51	Mindfulness app (Headspace)	Usual care	Total: 22.33% I: 36.54% C: 7.84%	Yes	No	Yes
Lengacher et al., 2018⁴⁹ USA (Florida)	Breast cancer	Total: 57 (9) Female only	Total: 15	mMBSR(BC)	N.A. (Single group)	13.3%	No	No	Yes
McGowan, 2019⁵⁰ Australia	Breast cancer	40-49 (4 participants) 50-74 (12 participants) Female only	Total: 16	eMBSR	N.A. (Nominal control group)	37.5%	No	No	No
Milbury et al., 2020⁵¹ USA	Metastatic lung cancer	I: 65.46 (8.8) C:65.40 (11.3) Male: 49% Female: 51%	Total: 51 I: 26 C: 25	Couple-based mindfulness meditation (CBM) via videoconference	Usual care	Total: 23.53% I: 26.92% C: 20%	Yes	No	Yes
Mosher et al., 2018⁵² USA (Indianapolis)	Metastatic breast cancer	I: 59.30 (11.95) C: 53.29 (10.93) Female only	Total: 47 I: 23 C: 24	Telephone-based ACT	Telephone-based education	Total: 17.02% I: 26.09% C: 8.33%	Yes	No	Yes
Mosher et al., 2019⁵³ USA (Indianapolis)	Advanced Lung Cancer	I: 63.20 (11.27) C: 62.00 (13.13) Male: 56% Female: 44%	Total: 50 I: 25 C: 25	Telephone-based ACT	Education	Total: 24% I: 20% C: 28%	Yes	No	Yes
Nissen et al., 2019⁵⁴ Denmark	Breast or prostate cancer	I: 55.11 (10.26) C: 56.22 (9.22) Male: 8.7% Female: 91.3%	Total: 150 I: 104 C: 46	Internet delivered MBCT	Waitlist control	Total: 26.67% I: 30.77% C: 17.39%	Yes	No	Yes
Price-Blackshear et al., 2020⁵⁵ USA	Breast cancer	I: 39.67 (5.44) Cl: 38.78 (5.08) Female only	Total: 117 I: 61 C: 57	Video-based C-MBI	Video-based I-MBI	Total: 31.62% I: 38% C: 26%	No	No	Yes
Rosen et al., 2017⁵⁶ USA (Texas)	Breast cancer	I: 51.40 (10.73) C: 53.22 (9.91) Female only	Total: 112 I: 57 C: 55	Mobile app-delivered mindfulness training (AMT)	Waitlist control	Total: 33.93% I: 45.61% C: 21.82%	Yes	No	Yes
Russell et al., 2019⁵⁷ Australia (Melbourne)	Melanoma	I: 53.5(12.1) C: 53.1 (15.2) Male: 46.4% Female: 53.6%	Total: 69 I: 46 C: 23	Online MBI	Usual care	Total: 10.14% I: 15.22% C: 0%	Yes	Yes	Yes
Zernicke et al., 2014⁵⁸ Canada (Alberta)	Mixed	I: 58 (8.2) C: 58 (13.0) Male: 27.4% Female: 72.6%	Total: 62 I: 30 C: 32	Online-MBCR	Treatment as usual	Total: 8.06% I: 16.67% C: 0	Yes	Yes	Yes

SD: standard deviation; ITT: intent to treat analysis; N.A.: not applicable; I: intervention group; C: control group; MBI: mindfulness-based intervention; MBSR: mindfulness-based stress reduction; MBCT: mindfulness-based cognitive therapy; MBCR: mindfulness-based cancer recovery; ACT: acceptance and commitment therapy.

Risk of bias assessment

Less than 25% of the studies had high risk of detection, attrition and reporting bias (Appendix C, Supplementary information). Conversely, more than 25% of studies reported high risks of selection biases and performance bias, attributed to single-group studies. All studies had an unclear or high risk of performance bias, stemming from the nature of the intervention which limits effective masking of the participants and personnel. Using the GRADE approach, the outcomes of anxiety and depression were rated as low while QoL and mindfulness are rated as very low (Appendix D, Supplementary information).

Effectiveness of eHealth-MBIs on anxiety

Twelve studies examined the effectiveness of eHealth-MBIs on anxiety. Six single-group studies were narratively summarized. Meta-analysis using Mean/SD (four RCTs) revealed a small pooled effect size (SMD = −0.28, 95% CI = −0.49, −0.06) (Figure 2). Homogeneity of effect sizes was observed across the four studies (Chi² = 2.06, p = 0.56, I² = 0%). Three RCTs had follow-up assessments. Results suggested that the short-term subgroup (≤3 months) had a larger effect size (SMD = −1.00, 95% CI = −2.39, 0.40) than the long-term subgroup (>6 months) (SMD = −0.39, 95% CI = −0.79, 0.00). However, such difference was not statistically significant (Chi² = 1.19, p = 0.55).

Figure 2.

Forest plot for anxiety (mean/SD).

Meta-analysis using change scores of mean (two RCTs, three comparisons) showed a small pooled effect size on anxiety (SMD = −0.13, 95% CI = −0.35, 0.09) (Figure 3). Homogeneity of effect sizes was observed across the two studies (Chi² = 0.53, p = 0.77, I² = 0%). One study had short-term follow-ups (≤3 months) and demonstrated a small effect size (SMD = −0.14, 95% CI = −0.71, 0.43). Additionally, six single-group studies found that eHealth-MBIs decreased anxiety with a small to large effect, with an SMD range of −0.20 to −6.81.^{41–43,45,49,50} (Appendix E, Supplementary information). Taken together, RCTs and single-group studies supported the effectiveness of eHealth-MBIs on anxiety.

Figure 3.

Forest plot for anxiety (change scores of mean).

Effectiveness of eHealth-MBIs on depression

Twelve studies used depression as an outcome and six of which were single-group studies. Meta-analysis using Mean/SD (four RCTs) revealed a small pooled effect size (SMD = −0.24, 95% CI = −0.54, 0.06) (Figure 4). Moderate heterogeneity of effect sizes was observed (Chi² = 5.94, p = 0.11, I² = 50%). Three RCTs had follow-up assessments. Subgroup analysis revealed that the short-term subgroup (≤3 months) had a larger effect size (SMD = −0.90, 95% CI = −2.39, 0.59) than the long-term subgroup (>6 months) (SMD = −0.47, 95% CI = −0.86, −0.07). However, the difference was not statistically significant (Chi² = 1.37, p = 0.50).

Figure 4.

Forest plot for depression (mean/SD).

Meta-analysis using change scores (two RCTs, three comparisons) showed a small pooled effect size on depression (SMD = −0.22, 95% CI = −0.45, −0.00) (Figure 5) and homogeneity of effect sizes was observed (Chi² = 0.09, p = 0.96, I² = 0%). One RCT had a short-term (≤3 months) follow-up assessment with a small effect size (SMD = −0.14, 95% CI = −0.72, 0.43).

Figure 5.

Forest plot for depression (change scores of mean).

Five single-group studies further supported that eHealth-MBIs had small to large effects in decreasing depression (SMD = −0.06 to −0.97)^{41–43,45,49,50} (Appendix E, Supplementary material). Additionally, Milbury et al.⁵¹ compared the effects of couple-based meditation (CBM), usual care (UC) and supportive-expressive (SE) intervention. Significant differences were not observed at post-intervention. However, after three months, CBM had the largest effect in decreasing depression compared to UC (F = 4.07, d = 0.53, p = 0.05) and SE group (F = 3.36, d = 0.59, p = 0.07). In short, RCTs and single-group, quasi-experiment research supported the effectiveness of eHealth-MBIs on depression.

Effectiveness of eHealth-MBIs on QoL

Six studies examined the effectiveness of eHealth-MBIs on QoL. Two single-group studies were narratively summarized. Meta-analysis was performed using four RCTs using mean/SD (Figure 6) and findings revealed no effect (SMD = 0.06, 95% CI = −0.44, 0.55). Considerable heterogeneity in effect sizes was observed (Chi² = 14.52, p = 0.002, I² = 79%). The short-term follow-up subgroup (≤3 months) had the largest effect size (SMD = 0.25, 95% CI = −0.29, 0.80) than the moderate-term (4–6 months) (SMD = 0.11, 95% CI = −0.21, 0.43) and long-term subgroups (>6 months) (SMD = 0.04, 95% CI = −0.29, 0.37). However, such differences did not achieve statistical significance (Chi² = 0.47, p = 0.92).

Figure 6.

Forest plot for quality of life (QoL) (mean/SD).

A subgroup analysis suggested that RCTs with UC had a larger pool effect size (SMD = 0.26, 95% CI = −0.16, 0.68) than the active-control subgroup (SMD = −0.77, 95% CI = −1.43, −0.10) with statistical significance (Chi² = 6.64, p = 0.01) (Figure 7). Furthermore, the female subgroup had a larger pooled effect size (SMD = 0.72, 95% CI = 0.28, 1.16) than the male subgroup (SMD = 0.11 95% CI = −0.21, 0.43) (Figure 8). However, the mixed-gender subgroup favoured the control (SMD = −0.35, 95% CI = −1.08, 0.38). The differences across the three subgroups were statistically significant (Chi² = 7.85, p = 0.02).

Figure 7.

Forest plot for quality of life (QoL) (mean/SD) (subgroup: control types).

Figure 8.

Forest plot for quality of life (QoL) (mean/SD) (subgroup: gender).

Additionally, a single-group study (McGown et al., 2019) showed small effect sizes (SMD = 0.11) at post-intervention and at three-month follow-up (SMD = 0.21). Another single-group study⁴⁹ measuring seven QoL domains had small to large effect sizes (SMD ranging from 0.09 to 0.65) (Appendix E, Supplementary material).

Effectiveness of eHealth-MBIs on mindfulness

Four RCTs examined the effectiveness of eHealth-MBIs on mindfulness and meta-analysis was performed (Figure 9). At post-intervention, findings revealed a small pooled effect size (SMD = −0.20; 95% CI = −0.89, 0.48), favouring the control group. Considerable heterogeneity was observed (Chi² = 23.89, p < 0.0001, I² = 87%). Two RCTs had a short-term follow-up assessment (≤3 months) and results revealed a small effect in increasing mindfulness (SMD = 0.29, 95% CI = −0.04, 0.61) with homogeneity (Chi² = 0.39, p = 0.53, I² = 0%). Furthermore, two single-group studies further supported the effectiveness of eHealth-MBIs on mindfulness with large effects (SMD = 0.78, 0.90)^43,49 (Appendix E).

Figure 9.

Forest plot for mindfulness (mean/SD).

Additionally, two RCTs examined five mindfulness subdomains.^44,58 Meta-analysis revealed no effects on the four subdomains of mindfulness: Observe, Describe, Non-judging, and Non-reacting (Appendix I, Supporting information). Nevertheless, the Awareness subdomain had small pool effect sizes at post-intervention (SMD = 0.19, 95% CI = −0.19, 0.58) and six-month follow-up assessment (SMD = 0.14, 95% CI = −0.19, 0.47) (Appendix I).

Subgroup analysis

Subgroups based on the types of comparisons, age groups and gender were performed for all outcomes. Results suggested no significant differences among the subgroups across all outcomes (Appendix F, G, H, I, Supporting information).

Publication bias

Funnel plots and statistical tests were not performed as any of the outcomes had at least 10 studies to ensure sufficient power in detecting asymmetry.⁵⁹ However, we minimized potential publication bias by conducting a comprehensive search on various databases to identify published studies and grey literature.⁶⁰

Discussion

This SR aimed to synthesize evidence concerning the effectiveness of eHealth-MBIs on cancer patients/survivors. Results suggested that eHealth-MBIs improved anxiety and depression among cancer patients/survivors. However, the effects on QoL and overall mindfulness were observed at short-term follow-up assessments (not at post-intervention). Furthermore, results from RCTs suggested that eHealth-MBIs improved the awareness subdomain of mindfulness at post-intervention and six-month follow-up assessment.

The eHealth-MBIs were effective in decreasing anxiety among cancer patients/survivors with a small effect size, which is consistent with previous SRs.^16,61 A possible explanation is that eHealth-MBIs increased ‘awareness’ towards the present-moment through techniques such as mindful breathing and body scan. As a result, emotional reactivity (typically evoked by anxiety symptoms) can be reduced.⁶² Additionally, mindfulness practices might promote ‘decentering’, which enable individuals to withdraw from emotional reactions to anxiety-provoking situations.^63,64 Similarly, a previous study found that mindful breathing was effective in reducing test anxiety among university students.⁶⁵ Furthermore, the included studies that reduced anxiety delivered their interventions weekly via smartphone and computer with internet; and the intervention duration ranged from four to eight weeks. In one study,⁵³ intervention contents included mindfulness perspective-taking, cognitive diffusion, acceptance, value clarification and committed actions. Other studies used Headspace, Happier and Calm applications to teach mindfulness techniques such as breathing exercise, body scan, noting and visualization.^47,48

This review indicated that eHealth-MBIs had a small effect size in decreasing depression, which is consistent with previous SRs.^16,61 The mechanisms of eHealth-MBIs are attributed to the inhibition of hypothalamic–pituitary–adrenal axis and glucocorticoid production, resulting in lower anxiety and depressive symptoms.⁶⁶ Furthermore, mindfulness practice might enhance a ‘non-judging’ attitude, resulting in diminished negative thoughts and negative emotions, including depression. In our review, the study that produced a large effect size involved intervention contents that were specifically tailored to cancer survivors, feedback sessions with therapists, daily exercise using smartphone-compliant website, high percentage of treatment adherence and long training duration (approximately 6.5 h weekly for eight weeks).⁵⁴

The eHealth-MBIs had no effect on improving QoL at post-intervention but had a small effect at short-term follow-up assessments (≤3 months) and this is consistent with previous SRs.^16,61 A possible explanation for the delayed effect is that the eHealth-MBIs might not have a direct and immediate effect on QoL. Instead, they had a mediating effect through other variables. Specifically, a previous research⁶⁷ elaborated that mindfulness enabled cancer patients to concentrate on the current moment, and therefore, detach themselves from negative thoughts and frequent ruminations. As a result, arousals from physical burden (linking with cancer symptoms) and psychological stress were ameliorated, leading to higher levels of QoL. Furthermore, mindfulness increased affect reactivity and cognitive function; and reduced perceived severity of cancer-related symptoms, contributing to better functioning in daily lives.⁶⁷

In this SR, a study that improved QoL delivered the App-delivered mindfulness training (AMT) whereby participants had a six-month subscription to access the AMT.⁵⁶ The intervention encompassed a 10-day foundation course and daily practice of mindfulness (including calming and insight meditations). Moreover, our review found that eHealth-MBIs had a larger effect on the female subgroup in improving QoL than male participants. A possible explanation is that female participants were more receptive to eHealth-MBIs, while male participants were more reluctant in verbalizing their emotional concerns and were less emotionally expressive.^68,69

Additionally, findings from single-group studies indicated that eHealth-MBIs increased mindfulness with a large effect, which is consistent with a previous SR.¹⁶ However, RCTs revealed only a small pool effect size at the short-term follow-up assessment (≤3 months). Furthermore, an RCT showed that the Awareness subdomain of mindfulness was improved at post-intervention and six-month follow-up. Learning mindfulness is a time-consuming process that requires regular practice of mindfulness techniques, which could explain the delayed effect found in this SR. Moreover, participants might initially encounter challenges (such as distractions) and unhelpful psychological outcomes (such as upsetting thoughts, disturbing emotions and boredom).⁷⁰ Without a controlled environment and guidance of an in-person instructor, mindfulness practice might be challenging.

In our review, a study that improved the overall mindfulness lasted eight weeks and delivered the intervention via smartphone/tablet.⁵⁶ Intervention contents entailed 10-day foundation training and mindfulness training (calming and insight meditation) using audio and videos.⁵⁶ Studies that enhanced the awareness subdomain took eight weeks and delivered via web cameras and telephone. The interventions comprised online lectures, online group discussion, in-session mindfulness practice and 35–45 min daily home practice of mindfulness, such as hatha yoga, loving-kindness medication, body scan and moving meditations.^44,58

Study limitations

This SR has some limitations that warrant cautions. First of all, there were limited studies per subgroups in the subgroup analyses, an ability to draw conclusions about differences in the consistency of intervention's effect among subgroups might be reduced.⁷¹ Secondly, as study outcomes were measured using various tools focusing on unrelated constructs, comparability of findings might be hampered.¹⁶ Thirdly, the included studies were conducted in North America, Oceania and Europe. Lastly, the exclusion of non-English studies might contribute to the omission of relevant studies in other languages.

Implications to clinical practice

This SR has implications to clinical practice and several eHealth-MBIs can be integrated into standard care for cancer patients/survivors. The intervention duration may range from four to eight weeks and may be delivered via smartphone, tablets and/or computer with internet on a weekly basis. Particularly, the eHealth-MBIs with Headspace, Happy or Calm mobile applications can be offered to enhance anxiety among participants. To ameliorate depression, the eHealth MBCT can be used with cancer-specific contents, feedback sessions with therapist and daily exercise using smartphone-compliant website. Targeting at QoL and mindfulness, the AMT can be offered to cancer patients/survivors with a combination of education component and self-practice of mindfulness via audios or animated videos. Finally, to enhance the awareness domain of mindfulness, online lectures, online group discussion and various techniques can be utilized (including hatha yoga, loving-kindness medication, body scan and moving meditations).

Implications to future research

The eHealth-MBIs can be promoted by policymakers and healthcare providers to cancer patients/survivors as a more cost-effective and accessible alternative than in-person MBIs. This is especially pertinent in the context of COVID-19 pandemic where cancer patients/survivors may have decreased access to face-to-face healthcare. Future research should examine eHealth-MBIs in regions of Asia, Africa and South America. Future SRs may examine the effectiveness of eHealth-MBIs based on different types of MBIs, cancer types and modes of delivery. Other outcomes (such as stress and post-traumatic growth) can be further explored.

Conclusion

This SR aimed to synthesize evidence concerning the effectiveness of eHealth-MBIs on cancer patients/survivors. Our findings suggested that eHealth-MBIs improved anxiety and depression at post-intervention, while the effects on QoL and mindfulness were observed at short-term follow-up assessments. Therefore, eHealth-MBIs can be offered as a cost-effective and accessible alternative in managing anxiety, depression, mindfulness and QoL among cancer patients/survivors. Additional research may examine the differential effects of the interventions, cancer types and modes of delivery using other study outcomes.

Supplemental Material

sj-docx-1-jtt-10.1177_1357633X221078490 - Supplemental material for Effectiveness of eHealth mindfulness-based interventions on cancer-related symptoms among cancer patients and survivors: A systematic review and meta-analysis

Supplemental material, sj-docx-1-jtt-10.1177_1357633X221078490 for Effectiveness of eHealth mindfulness-based interventions on cancer-related symptoms among cancer patients and survivors: A systematic review and meta-analysis by Jonathan Ying Ting Fung, Helen Lim, Nopporn Vongsirimas, and Piyanee Klainin-Yobas in Journal of Telemedicine and Telecare

Footnotes

Author contributions

The authors confirm contribution to the paper as follows: JF collected, appraised and extracted data; performed data analyses; and drafted the manuscript. HL collected appraised and extracted data. PKY and NV served as supervisors for JF and HL and finalized the manuscript. All authors reviewed, discussed the results and approved the final manuscript.

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) received no financial support for the research, authorship and/or publication of this article.

ORCID iD

Piyanee Klainin-Yobas

Supplemental material

Supplemental material for this article is available online.

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