Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review

PhDTara Donker firstname.lastname@example.com
Katherine Petrie
Bscpsych
Hons
PhD;Judy Proudfoot
PhD;Janine Clarke
MPHMary-Rose Birch
PhDHelen Christensen
PhDTara Donker

Black Dog Institute
Black Dog Institute
University of New South Wales
SydneyAustralia

University of New
South Wales Hospital Road Prince of Wales Hospital Sydney2031Australia

Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review
10.2196/jmir.2791Review Corresponding Author:
Background:The rapid growth in the use of mobile phone applications (apps) provides the opportunity to increase access to evidence-based mental health care.Objective: Our goal was to systematically review the research evidence supporting the efficacy of mental health apps for mobile devices (such as smartphones and tablets) for all ages.Methods:A comprehensive literature search (2008)(2009)(2010)(2011)(2012)(2013) in MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, PsycINFO, PsycTESTS, Compendex, and Inspec was conducted. We included trials that examined the effects of mental health apps (for depression, anxiety, substance use, sleep disturbances, suicidal behavior, self-harm, psychotic disorders, eating disorders, stress, and gambling) delivered on mobile devices with a pre-to posttest design or compared with a control group. The control group could consist of wait list, treatment-as-usual, or another recognized treatment. Results: In total, 5464 abstracts were identified. Of those, 8 papers describing 5 apps targeting depression, anxiety, and substance abuse met the inclusion criteria. Four apps provided support from a mental health professional. Results showed significant reductions in depression, stress, and substance use. Within-group and between-group intention-to-treat effect sizes ranged from 0.29-2.28 and 0.01-0.48 at posttest and follow-up, respectively.Conclusions:Mental health apps have the potential to be effective and may significantly improve treatment accessibility. However, the majority of apps that are currently available lack scientific evidence about their efficacy. The public needs to be educated on how to identify the few evidence-based mental health apps available in the public domain to date. Further rigorous research is required to develop and test evidence-based programs. Given the small number of studies and participants included in this review, the high risk of bias, and unknown efficacy of long-term follow-up, current findings should be interpreted with caution, pending replication. Two of the 5 evidence-based mental health apps are currently commercially available in app stores.Res 2013;15(11):e247)(J Med Internet

Introduction mobile devices [2], and around 13,600 health apps intended for use by consumers were available for download in Apple's App Store [3]. About 6% of these apps targeted mental health outcomes, while 18% focused on related health issues, such as sleep, stress, relaxation, and smoking behaviors. A survey among the Australian general public indicated that 76% would be interested in using mobile phones for mental health monitoring and self-management [4]. This suggests that mHealth is acceptable and may be a useful vehicle for enhancing access to evidence-based monitoring and self-help for individuals with mild-to-moderate common mental health conditions [4]. Clinical practice guidelines recommend cognitive behavior therapy (CBT) and self-help resources (such as mHealth) as options for psychological treatment for individuals experiencing mild-to-moderate symptoms of anxiety or depression [5]. mHealth apps can be used as stand-alone self-help programs or as a conjunctive treatment modality in guided programs, for example, part of a website or through direct contact with a mental health professional. The app can include treatment components such as cognitive therapy (CT), behavioral activation (BA), psychoeducation, or monitoring of symptoms.

Advantages of mHealth include the improvement of treatment accessibility and participant retention, real-time symptom and activity monitoring and tracking of treatment progress through ecological momentary assessment (EMA), provision of personalized feedback and motivational support, portability and flexibility of use, and the potential to improve adherence to treatment [6][7][8][9][10]. However, there are also disadvantages with using mobile devices for mental health. Technical problems and factors related to telecommunication can arise (eg, battery failures, reliability and sustainability of connections [11]), and issues of data security, patient privacy, and the identification and timely management of crises and risk of harm must be carefully considered when integrating smartphone technology into behavioral health care [12].

Previous research suggests that mental health interventions delivered through mobile apps can be effective in treating a range of mental health disorders, such as depression, stress, anxiety, and smoking cessation [6,7]. However, the thriving development of mental health apps warrants a systematic review of the available evidence base in this growing area. Previous reviews examining evidence-based mental health apps did not incorporate quantitative analyses [12] or included mHealth interventions that were not directly downloadable as an app (such as programs using SMS [short message service] text messaging or Internet-enabled interventions on mobile phones [6,13]). Therefore, the aim of this paper is to systematically review the available evidence-based apps directly downloadable on mobile devices (such as smartphones and tablets) for mental health symptoms or disorders (depression, anxiety, substance use, sleep disorders, suicidal behavior, psychotic disorders, eating disorders, stress, gambling) in children, adolescents, adults, and older individuals.

Methods

Search Strategy and Selection of Studies

A comprehensive literature search in bibliographic databases (MEDLINE, Embase, the Cochrane Central Register of Controlled Trials, PsycINFO, PsycTESTS, and Compendex and Inspec) for relevant articles published from January 1, 2008 (launch date of the first app), to May 30, 2013, was conducted. Terms indicative of mobile apps and mental health disorders were used to search these databases, with the search being limited to "humans", English, and peer-reviewed journals (see Multimedia Appendices 1-3 for the full search string). The identified titles and abstracts were screened for eligibility by 2 independent researchers. Full text copies of all potentially relevant papers, or papers where there was insufficient information in the abstract to determine eligibility, were obtained. Full text articles were further screened and discarded from further analyses if they met exclusion criteria. In addition, references of earlier reviews and reference lists of the included primary articles were examined. Furthermore, key technology journals (Cybertechnology, Behavior and Social Networking; Journal of Medical Internet Research; and Studies in Health Technology and Informatics) were hand-searched. We also reviewed Beacon, a website for evidence-based online programs for mental health, developed and delivered by the Centre for Mental Health Research at the Australian National University. Finally, a search was conducted of prominent individual authors' and researchers' names in the field of mHealth or Internet interventions (see Multimedia Appendix 4) in MEDLINE. Data extraction of relevant articles was completed by 2 independent researchers, with disagreements resolved through discussion or with a third researcher.

We applied strict inclusion criteria in order to investigate any evidence-based mental health apps that could be downloaded from app stores (eg, Google Play for Google Android [14] or the Apple iTunes store [15]). Studies examining the effects of mental health apps on mental health symptoms or disorders (depression, anxiety, substance use, sleep disorders, suicidal behavior, self-harm, psychotic disorders, eating disorders, stress, and gambling) that were directly downloadable on a mobile device (eg, smartphone or tablet) compared with a control group were included. The control group could consist of a wait list, treatment-as-usual, or another treatment. Studies without a control group (pre-post design) were also included. There was no restriction on participant age. Studies were excluded if they did not include an intervention or if mental health symptoms/disorders were not an outcome, and if the intervention was an Internet-based intervention, virtual reality exposure treatment, interactive voice response technology intervention, or a text messaging-only intervention without a mobile application component. Studies were also excluded if the intervention was downloaded on a computer and transferred (eg, through Bluetooth or infrared) to a mobile device, if the intervention targeted a medical disorder (eg, irritable bowel syndrome, diabetes), if the paper provided a description of the mobile application but no outcome data, and if the intervention was developed before 2008. Conference abstracts, protocol papers, case studies, non-peer reviewed papers, and non-English papers were also excluded.

Quality Assessment

Study quality was assessed according to 6 basic criteria of the Cochrane Risk of Bias Assessment Tool [16]: sequence generation, allocation concealment, blinding of outcome assessors, incomplete outcome data, selective outcome reporting, and other sources of bias. For the third criterion (blinding of outcome), we omitted blinding of participants since blinding participants for treatment allocation is rarely achievable in intervention trials for mental health disorders.

Outcome Measures

Primary outcome measures included reduction of depression symptoms, anxiety symptoms, substance use, sleep disturbance, suicidal behavior (suicide ideation, suicide plans, and attempts), self-harm, psychotic symptoms, symptoms of eating disorders, and gambling, as assessed with validated mental health scales.

Statistical Analyses

When data were available and extractable, intention-to-treat (ITT) within-group and between-group effect sizes (Cohen's d) for the intervention group were calculated by taking the difference between the mean pre-and posttest scores (within-group effect size) or the difference of the posttest scores (between-group effect size) and dividing by the pooled standard deviation. Effect sizes of 0.8 can be assumed to be large, while effect sizes of 0.5 are moderate, and effect sizes of 0.2 are small [17]. Where authors provided only t test statistics, we computed effect sizes using the formula: d=t / sort(df) [18]. Hedges' g effect sizes were converted to Cohen's d. Authors were contacted to provide additional data if needed. Two studies [19,20] did not provide sufficient data to calculate ITT within-group effect sizes.

Results

Selection and Inclusion of Studies

A total of 5464 abstracts in MEDLINE (n=1859), Embase (n=1030), the Cochrane Central Register of Controlled Trials (n=277), PsycINFO (n=1095), PsycTESTS (n=1), and Compendex and Inspec (n=1203) were examined (N=4997 abstracts in total, after removal of duplicates). The majority of records that were excluded addressed nonpsychological technical issues, provided descriptions of mobile apps without outcome data, or were protocol papers or conference abstracts. Of these, 133 full text papers potentially eligible for inclusion were retrieved for further consideration, of which 126 were excluded. Seven trials met inclusion criteria. A further screening for potentially relevant references in recent systematic reviews or meta-analyses and the included studies, individual author names in MEDLINE, and hand-searching of technology journals (Cybertechnology, Behavior and Social Networking; Journal of Medical Internet Research; and Studies in Health Technology and Informatics [January 1, 2008, to May 30, 2013]) and the Beacon website resulted in 95 potentially relevant abstracts and retrieval of 64 additional full text papers for further assessment. Of these, only 1 study met inclusion criteria and was included in the final analysis. In total, 8 trials were identified. These described 5 apps (Mobilyze! [11], mobiletype [21,22], DBT Coach [23], Mobile Stress Management [19,20,24], and Get Happy Program [25]) (see Figure 1 for a flowchart of the screening process). There was a high degree of consensus among raters who screened the titles and abstracts (an interrater reliability of 95.2%).

Characteristics of Included Studies

A total of 227 participants were recruited across all studies. One study [19] did not provide sufficient information about sample size per treatment arm. Of the 8 included studies, 4 trials describing 3 apps assessed depression (Mobilyze!, mobiletype, Get Happy Program), and 3 studies describing 1 app (Mobile Stress Management) assessed stress as a primary outcome measure. Substance use was used as an outcome measure in 1 study (DBT Coach). Table 1 provides an overview of the included studies (see Multimedia Appendix 5 for the complete version of the table). One study used BA and another used CBT as the therapeutic mode of the intervention. Two studies described a trial delivering emotional self-awareness (ESA), 1 study was based on dialectical behavioral therapy (DBT) and opposite action (ie, emotional regulation skills), and 3 studies described an app delivering stress inoculation training (SIT) as the content of the intervention. Four studies describing 3 trials used an attention-placebo as a control group, 1 study used an active comparison, and 1 study did not specify the nature of the control group. Two studies used a pre-post design without a control group, and all studies except one were feasibility and/or pilot studies. Two studies recruited adults from the community, 1 study recruited from an outpatient clinic, and 2 studies recruited from the workplace. Two studies describing 1 trial recruited adolescents from general practice, and 1 study targeted female university students. Four studies delivered the intervention through a stand-alone mobile app, while 3 studies describing 2 trials used a mobile app alongside a website and EMA to deliver the intervention. One study used a mobile application in conjunction with traditional face-to-face therapy. All included studies delivered the program on a mobile phone, with 1 study also including iPads. Delivery length varied between 6 days and 8 weeks. Five studies assessed posttest outcomes only, whereas 3 studies describing 2 trials undertook follow-up assessments as well (6 weeks and 3 months). Five studies describing 4 apps were guided by mental health professionals through phone or email contact, whereas in 3 studies describing 1 app, participants independently navigated their way through the trial.

Quality Assessment

The quality of the studies varied but was generally low (see Table 2). Three studies describing 2 apps reported adequate sequence generation [21,22,25], whereas 3 studies [19,20,24] did not outline their sequence generation method. Three studies [21,22,25] reported allocation to conditions by an independent (third) party, whereas 3 other studies [19,20,24] did not provide sufficient information on allocation. Two studies that included diagnostic interviews [21,22] reported using blinded outcome assessors, and 4 studies [19,20,24,25] did not report blinding of assessors or used self-report outcome measures. Two studies [11,23] were not eligible for ratings for sequence generation, allocation concealment, or blinding of outcome assessors due to the pre-post study design. In 6 studies [11,[21][22][23][24][25], ITT analyses (completeness of follow-up data) were conducted; 1 of these failed to describe dropout rates [23], and only 1 study [11] described reasons for dropout during the intervention. Two studies [19,20] did not state the nature of the statistical analyses or dropout rate at all. Insufficient information and a high risk of bias of selective outcome reporting was present in 3 studies [19,20,23] and 2 studies [21,22] respectively. Three studies [11,23,25] had a high risk of other sources of bias (eg, absence of a control group, possible treatment infidelity) while for 5 studies [19][20][21][22]24] the risk of bias from other sources was unclear (due to significant difference at baseline for stress outcome, unequal number of participants in intervention and control group, and insufficient information). None of the included studies met all 6 quality criteria of the Cochrane tool (see Table 2).

Effects of the Mental Health Apps

Depression

Four studies describing 3 mobile apps [11,21,22,25]  In a randomized controlled trial (RCT) of a guided mobiletype app with EMA conducted by Kauer et al [21] and Reid et al [22], no significant differences were found at posttest and follow-up on outcomes of depression, anxiety, and stress among adolescents from general practice compared to an attention control group (Depression and Anxiety Stress Scale [DASS] anxiety: d=0.07, P=.76; DASS depression: d=0.09, P=.69). However, it should be noted that the control group received largely the same intervention as the experimental group, with the exception of two components; ESA training via EMA and minimal feedback reports. Mediator analyses yielded an indirect effect of group on depression via ESA (beta=-0.610, 95% CI -5.596 to -0.003). Significant small to moderate within-group differences over time were found for the intervention group

Anxiety/Stress

Three RCTs describing 1 unguided mobile app (Mobile Stress Management) using SIT [19,20,24] found a significant decrease in state and trait anxiety (State and Trait Anxiety Inventory [STAI]) and a significant increase in active coping skills among oncology nurses [20,24] and female university students [19] compared to a control group. Grassi et al [19] used a simplified version of the Mobile Stress Management app, which was also effective for reducing stress. However, both Villani et al [20] and Grassi et al [19] did not provide statistical results for intervention versus control group comparisons. Villani et al [24]

Substance Use

A pilot feasibility study aiming to reduce substance use (alcohol, drugs, and tobacco) among adults suffering from borderline personality disorder using a mobile app (DBT Coach [23]) in conjunction with face-to-face DBT therapy, indicated a significant reduction (P<.05) within each DBT Coach session in emotional intensity and urge to use substances (d=0.52 and d=0.29 respectively). Furthermore, a significant reduction (P<.05) in symptoms of depression (BDI: P=.014, d=0.55), global symptom severity (Brief Symptom Inventory: P=.021, d=0. 43), and confidence in participants' ability to use opposite action (ie, emotion regulation) skills (Behavior Confidence Questionnaire: P=.008, d=0.59) was noted from pre-to post assessment. Multimedia Appendix 5 outlines the ITT within-group effect sizes. The DBT Coach app is publicly available for download.

Ecological Momentary Assessment

Mixed findings were obtained from the 2 studies using EMA as part of the intervention. In the Burns et al [11] study, promising accuracy rates (60-91%) were achieved in predicting categorical contextual states (eg, location) based upon participant EMA entries. For participant states rated on continuous self-report scales (eg, mood), predictive capability was poor. Notwithstanding these technological outcomes, Reid et al [21] and Kauer et al [22] demonstrated that increased self-monitoring with EMA by participants did lead to increased ESA and thereby reduced depressive symptoms.

Intervention Feasibility and Adherence

Three studies providing usability and feasibility outcomes (eg, acceptability of the technology, perceived usefulness, perceived utility) reported moderate to high rates of mobile phone usage, feasibility, and participant satisfaction with the intervention [11,23,25]. The dropout rate was reported in 4 studies and varied between 12.5% and 34.3% [11,21,22,25]. Reported reasons for dropout, where described, were mostly due to technical problems [11].

Discussion

Principal Results and Comparison With Prior Work

In general, the studies included in this systematic review showed promising results for evidence-based mental health apps in reducing depressive symptoms and caseness, stress, anxiety, and substance use, similar to previous reviews of mHealth [6,7]. However, due to the high risk of bias in some studies, these findings need to be considered with caution pending replication. Due to the absence of a control group in 2 studies [11,23], it was difficult to determine whether the beneficial effects were attributable to the app itself, a function of natural remission or regression to the mean, or in case of the DBT Coach app [23], due to the face-to-face DBT therapy offered to all participants in conjunction with the app. Additionally, a clear conclusion about the efficacy of the DBT Coach for substance use treatment cannot be drawn yet, since-besides the absence of a control group-change in substance use (eg, amount of alcohol units per week) prior to or after treatment was not reported, nor was a distinction made between different types of substance use (alcohol, drugs, nicotine cessation). Furthermore, some studies failed to provide sufficient information regarding dropout rates or did not report the statistical analyses used [19,20].

The mobiletype app was the only intervention that failed to yield any significant direct effect on depression, although a significant indirect effect was found in a reduction of depressive symptoms through the direct effect of increased ESA [21]. Because the attention-placebo control group received almost the same intervention as the experimental group, except for the ESA component, the nonsignificant finding is likely to be the cause of this finding. This study suggests that repeated self-monitoring over time using EMA on a mobile device may increase ESA and thereby reduce depressive symptoms. Evidence supports a similar mechanism underlying improvements in depression with CBT, where one of the most important components of CBT for depression involves rating one's mood and activities in a diary to raise awareness of how activities influence mood states [26]. The development of mobile devices has facilitated the collection of EMA data, thereby providing a portable and convenient delivery mode with which an individual can incorporate EMA and regular mood monitoring in their daily lives and improve ESA as part of treatment for depression. Although EMA shows promising results in predicting categorical contextual states, it needs to be further optimized to be able to accurately predict mood states [11]. Once refined in such a way to maximize accuracy and temporal resolution and minimize bias, EMA holds considerable potential to reveal dynamic interplay between mood, cognition, and behavior, increase participant self-awareness of such processes, and thereby enhance mental health treatment [27]. Together with the use of biomedical and/or activity sensors, timely personalized feedback can be generated to prompt users. mHealth interventions therefore have the potential to improve current depression treatment considerably [10]. In a similar way to guided Internet interventions [28], guided apps might derive larger effect sizes and adherence rates than stand-alone self-help apps, but more research is necessary to elucidate this.

Usability, Helpfulness, and Satisfaction

Usability, helpfulness, and satisfaction ratings, where assessed, were moderate to high [11,23,25], indicating that mHealth apps are perceived to be a useful vehicle for enhancing access to evidence-based monitoring and self-help. However, common technical problems (eg, battery failure, connectivity, freezing of app) need to be overcome. Adherence rates (if reported) were high, in line with previous research in mHealth [29], but higher when compared to adherence rates seen with Internet-based interventions [30]. It might be that the method of delivery (mobile phone) and its portability and flexible usage, and/or its delivery of personalized feedback may account for these higher retention rates for mobile apps. However, some of the included studies provided subjects with monetary rewards for participation, which is likely to artificially raise adherence rates as well.

Sustainability of Results

Most studies included only posttest assessment or a short-term follow-up (6 weeks). Although 1 study showed sustainable results at 3-month follow-up [25], sustainability of results over a medium-to long-term timeframe requires further investigation and replication. As such, on the basis of current evidence, sustainability of results cannot yet be determined.

Since mental health apps downloadable for use by the general population are increasing rapidly, despite evidence for their efficacy being largely unknown, the focus of this systematic review was on apps only. We applied very stringent inclusion criteria to ensure that we identified the evidence-based mental health apps that could be downloadable in the future by the general public from app stores, for example, Google Play for Google Android [14] or the Apple App Store [15]. Therefore, several highly sophisticated programs using mobile technology were excluded, such as the myCompass program [31] for depression, anxiety, and stress. The CBT-based myCompass program is delivered via a website with an Internet-enabled mobile phone component and encourages real-time self-monitoring of moods, mood triggers, and lifestyle behaviors using SMS text messaging and email prompts. Other examples of similar programs include an SMS-based txt2quit intervention [32] and a video-based STUB IT intervention [33], both of which have been shown to be effective for smoking cessation, and an SMS-based intervention [34] to increase medication adherence in individuals with schizophrenia. We were also unable to include the innovative INTREPID research [35], which used virtual reality exposure therapy on mobile phones to reduce anxiety.

There are more than 3000 mental health apps for Android, Apple, and Microsoft freely available to download to date, compared to the 8 evidence-based apps we identified through our systematic review. Only 2 of the apps included in this review are currently available for public download, comprising less than 1% of the commercially available apps. A recently published review on existing (commercial) mHealth apps for the most prevalent health conditions in the Global Burden of Disease list provided by the World Health Organization [36] echoes this finding. The authors concluded that the development of mHealth apps was first and foremost driven by commercial and economic motivations rather than scientific motivations behind research. Although the numerous protocols [9,37] and case studies [38,39] we excluded indicate a nascent field of research, the rapid growth and development of thousands of non-evidence-based mental health technologies has generated the need for independent regulation. This is underlined by the alarming findings from previous research [40][41][42] indicating that only 13-26% of Web-based or app-based interventions for smoking cessation adhere to treatment guidelines. A recent study on commercial apps using EMA for alcohol use echoes these findings [43]. The US Food and Drug Administration has taken an important step towards the development of quality control guidelines for health apps [44], but there are still major issues and dangers concerning the lack of quality control of commercially available mental health apps. Further research and work must be undertaken to develop, test, and disseminate evidence-based mHealth interventions among the public to ensure optimal public health outcomes.

Limitations

This review has several limitations. First, despite an extensive search, the number of included studies was small, which restricted our interpretations as to whether mHealth apps have an effect on reducing mental health symptoms. Second, the number of participants in the included studies was small. As a result, the studies were probably underpowered to detect the more subtle effects of the interventions. Furthermore, small sample sizes hamper the precision and accuracy of the statistical results and therefore limit our interpretations [45]. Third, the quality of the included studies was low. Historically, low quality trials yield positive results [46]. Due to the small number of studies, we were unable to examine whether significant differences existed between higher-and lower-quality studies. Fourth, there were no studies that examined the long-term efficacy of mental health apps. Therefore, long-term effects remain as yet unknown. Finally, only studies from peer-reviewed, English language journals were included in this review. However, the effect of language bias has been shown to have a minimal impact on the conclusions of systematic reviews [47].

Future Research

There is a very clear need for more research in this area. Trials with an RCT design of high quality to minimize risk of bias are needed to determine the efficacy of mental health apps. Unfortunately, the competitive nature and time-consuming process of grant applications and RCT designs necessary for such high-quality research contrasts sharply with the speed of development in this highly innovative technology. Component testing with small sample sizes may offer one solution to help bridge the gap between academia and real-world applications [48]. Research is particularly weak in the domains of sleep disturbance, anxiety disorders, and smoking cessation and needs further investigation. The cost-effectiveness and cost-utility of mHealth, compared to standard care or Internet-based treatment, requires further examination.

Conclusions

In summary, although a firm conclusion cannot yet be drawn, the current systematic review suggests that mobile apps for mental health have the potential to be effective in reducing depression, anxiety, stress, and possibly substance use for individuals experiencing these symptoms. Given the widespread usage of mobile and smartphones and increasing uptake of tablet devices, mHealth has the potential to increase treatment accessibility globally. The difference in the volume of commercial apps compared to the small number of tested evidence-based apps is striking. It warrants the need for public education and further development and research into evidence-based mental health apps and consideration of industry regulation.

Figure 1 .
1Flow diagram of participants.

risk of bias; 1: insufficient information; 2: high risk of bias; NA: not applicable.

(DASS stress: d=0.37 at posttest; d=0.59 at follow-up; DASS anxiety: d=0.31 at posttest; d=0.45 at follow-up; DASS depression: d=0.34 at posttest; d=0.64 at follow-up) and control group (DASS stress: d=0.14 at posttest; d=0.41 at follow-up; DASS anxiety: d=0.07 at posttest; d=0.08 at follow-up; DASS depression: d=0.42 at posttest; d=0.61 at follow-up). Between-group effect sizes were small and nonsignificant (DASS stress: d=0.14 at posttest; d=0.22 at follow-up; DASS Anxiety: d=0.25 at posttest; d=0.07 at follow-up; DASS depression: d=0.11 at posttest; d=0.09 at follow-up). The mobiletype app is not publicly available for download to date. Watts et al [25] found a significant reduction over time (P<.001) and large effect sizes in a pilot RCT of a partially guided CBT-based program for depression delivered either via a computer or mobile app (Get Happy Program) (PHQ-9: d=1.56; Beck Depression Inventory [BDI-II]: d=1.90; Kessler10 [K10]: d=1.93). No differences between the two groups were found for depression over time with P>.05 (PHQ-9: P=.34, d=-0.14; BDI-II: P=.52, d=-0.11; K10: P=.90, d=-0.01). The Get Happy app is not publicly available for download to date.

Table 1 .
1Psychosocial studies of applications on mobile devices (Intention-To-Treat). App: Application; BA: Behavioral Activation; BCQ: Behavior Confidence Questionnaire; BDI: Beck Depression Inventory; BPD: Borderline Personality Disorder; BSI: Brief Symptom Inventory; COPE: COPE Inventory; DASS: Depression, Anxiety, and Stress Scale; DBT: Dialectical Behavior Therapy; ESA: Emotional Self Awareness; F2F: Face-to-Face; GAD-7: Generalized Anxiety Disorder-7 item scale; GP: General Practitioner; It: Italian; K10: Kessler Psychological Distress Scale-10 item scale; MDD: Major Depressive Disorder; MHP: Mental Health Professional (psychologist or psychotherapist, GP); MINI: Mini-International Neuropsychiatric Interview; NA: Not Applicable; OA: Opposite Action; PHQ: Patient Health Questionnaire; QIDS-C: Quick Inventory of Depression Symptoms-Clinician Rated; RCT: Randomized Controlled Trial; SIT: Stress Inoculation Training; STAI: State-Trait Anxiety Inventory.Within f and between g effect size (Cohen's d) d=0.31 b,f d=0.45 b,d,f d=0.25 a,g d=0.07 b,g DASS Depression: Mobile-type BDI: d=0.55 a,d,f BSI: d=0.43 a,d,f BCQ: d=0.59 a,d,f Emotional intensity to use substance : d=0.52 c,d,f Urge to use substance: d=0.29 c,d,f 10-14 days;Delivery length

and support

Delivery
type

Control
group

Intervention
group

Study
sample

Primary out-
come mea-
sure

Trial
Author (year);
name of app

PHQ-9: d=1.95 a,e,f

d=2.28 a,e,f

GAD-7: d=1.37 a,e,f

8 weeks;

MHP

Mobile app
+ website +
EMA on mo-
bile phone

NA
n=8;

BA

Adults
from the
com-mu-
nity

MDD
Pre-
post
pilot

Burns et al
(2011);

Mobilyze!

DASS Stress:

d=0.37 a,f

d=0.59 b,e,f

d=0.14 a,g

d=0.22 b,g

DASS Anxiety:

d=0.34

d=0.64 b,e

d=0.11 a,g

d=0.09 b,g

ESA:

d=0.31 a

d=0.66 b,d

d=0.09 a,e,g

d=0.58 b,d,g

8 modules over
2-4 weeks; MHP

Stand-alone
mobile app +
EMA on mo-
bile phone

n=49;

Attention
control +
part of
the indi-
vidual-
ized data
summary
reports +
meeting
with GP

n=68;

ESA + Individual-
ized data summa-
ry reports + meet-
ing with GP

Adoles-
cents
from gen-
eral prac-
tice

MDD
RCT
Kauer et al
(2012);

Reid et al
(2011);

MHP

Mobile app
on mobile
phone + F2F
DBT

NA
N=21;

DBT + OA

Adults
from out-
patient
clinic

BPD and
substance
use

Pre-
post

pilot

Rizvi et al
(2011);

DBT Coach

NA
8 videos over 4
weeks; no sup-
port

Stand-alone
mobile app
on mobile
phone

n=8;

Attention
control

n=8;

SIT

Female
oncology
nurses

Stress
RCT
Villani et al
(2012);

Mobile Stress
Management

STAI (anxiety trait):

d=0.41 a,d,f

COPE (Active): d=-0.45 a,d,f

COPE (Denial): d=0.53 a,d,f

8 videos over 4
weeks; no sup-
port

Stand-alone
mobile app
on mobile
phone

n=15; At-
tention
control

n=15;

SIT

Female
oncology
nurses

Stress
RCT
Villani et al
(2011);

Mobile Stress
Management

Table 2 .
2Risk of bias assessed by Cochrane Risk of Bias Tool a .Total
Other sources
of bias

Selective outcome
reporting

Incomplete outcome
data

Blinding
Allocation conceal-
ment

Sequence gener-
ation

targeted depression. Burns et al [11] found a significant reduction in depression caseness (Mini-International Neuropsychiatric Interview [MINI]: Z=2.15, beta [week]=-.65, P=.03), as well as depression and anxiety symptoms at posttest (Patient Health Questionnaire [PHQ-9]: d=1.95, P<.001; Quick Inventory of Depression Symptoms-Clinician Rated: d=2.28, P<.001; Generalized Anxiety Disorder-7 item scale: d=1.37, P<.001) in a pilot test of the guided Mobilyze! app alongside a website and EMA for adults from the general population. The Mobilyze! app will be publicly available for download soon.

reported significant decreases in state anxiety over time (F 1,28 =71.365, P≤.001) and a significant group x time interaction effect for state anxiety (F 1,28 =27.476, P≤.001). Within-group effect sizes (converted from t test statistics) were small for active coping strategies (COPE Inventory [COPE] Active: d=0.42), and large for state anxiety (STAI: d=0.84) and denial coping strategies (COPE Denial: d=1.08). The Mobile Stress Management app is publicly available for download (Italian version only).
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AcknowledgmentsThis study is funded by Black Dog Institute, University of New South Wales. HC is supported by National Health and Medical Research Council Fellowship 525411.Conflicts of InterestNone declared.Multimedia Appendix 1Search string MEDLINE and Embase.[GIF File, 40KB-Multimedia Appendix 1]Multimedia Appendix 2Search string Cochrane Compendex and Inspec.[GIF File, 52KB-Multimedia Appendix 2]Multimedia Appendix 3Search string PsycINFO and PsycTESTS.[GIF File, 75KB-Multimedia Appendix 3]Multimedia Appendix 4Individual author names.[PDF File (Adobe PDF File), 26KB-Multimedia Appendix 4]Multimedia Appendix 5Psychosocial studies of applications on mobile devices (intention-to-treat).
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13; peer-reviewed by J Ruwaard, O Kristjansdottir. Edited, Carlbring, submitted 25.06.. comments to author 21.08.13; revised version received 17.09.13; accepted 18.09.13; published 15.11.13Edited by P Carlbring; submitted 25.06.13; peer-reviewed by J Ruwaard, O Kristjansdottir; comments to author 21.08.13; revised version received 17.09.13; accepted 18.09.13; published 15.11.13

Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review. T Donker, K Petrie, J Proudfoot, J Clarke, M R Birch, H Christensen, 10.2196/jmir.279124240579J Med Internet Res. 1511247Please cite as: Donker T, Petrie K, Proudfoot J, Clarke J, Birch MR, Christensen H Smartphones for Smarter Delivery of Mental Health Programs: A Systematic Review J Med Internet Res 2013;15(11):e247 URL: http://www.jmir.org/2013/11/e247/ doi: 10.2196/jmir.2791 PMID: 24240579

), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information. Katherine ©tara Donker, Judy Petrie, Janine Proudfoot, Mary-Rose Clarke, Helen Birch, Christensen, 15.11.2013. This is an open-access article distributed under the terms of the Creative Commons Attribution License. as well as this copyright and license information must be included©Tara Donker, Katherine Petrie, Judy Proudfoot, Janine Clarke, Mary-Rose Birch, Helen Christensen. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 15.11.2013. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.


VIRULENCE AND PRE-LETHAL REPRODUCTIVE EFFECTS OF Metarhizium anisopliae var. anisopliae ON Pseudococcus viburni (HEMIPTERA: PSEUDOCOCCIDAE)
OCTOBER-DECEMBER 2011

Aldo Pereira
Facultad de Agronomía
Universidad de Concepción
Casilla 537Chillán

Pedro Casals
Facultad de Agronomía
Universidad de Concepción
Casilla 537Chillán

Ana María Salazar
Instituto de Investigaciones Agropecuarias INIA
Casilla 426ChillánChile

Marcos Gerding
Instituto de Investigaciones Agropecuarias INIA
Casilla 426ChillánChile

VIRULENCE AND PRE-LETHAL REPRODUCTIVE EFFECTS OF Metarhizium anisopliae var. anisopliae ON Pseudococcus viburni (HEMIPTERA: PSEUDOCOCCIDAE)

CHILEAN JOURNAL OF AGRICULTURAL RESEARCH
714OCTOBER-DECEMBER 2011Received: 15 March 2011. Accepted: 25 August 2011.554 RESEARCHLethal doselethal timebiological controlentomopathogenic fungusfecundityfertilitylongevity
Obscure mealybug, Pseudococcus viburni (Signoret), is a cosmopolitan pest that causes damage by suction of vascular juices and the production of honeydew, as well as for being a quarantine insect. Within control options, entomopathogenic fungi are a good alternative, nevertheless, more research is needed. In this research, the Metarhizium anisopliae var. anisopliae (Metschnikoff) isolate Qu-M984 was evaluated on P. viburni under laboratory conditions. Virulence was evaluated by lethal dose 50 (LD 50 ) and lethal time 50 (LT 50 ), for each of the four life stages of the female. The doses tested were 10 5 , 10 6 , 10 7 and 10 8 conidia mL -1 . Also fecundity, egg size, fertility, and longevity of adult females were evaluated at doses of 10 5 and 10 6 conidia mL -1 , the evaluations were made every 2 d throughout the insect life time. The LD 50 and LT 50 obtained were variable for each life stage, although without statistical differences among life stages (P > 0.05), ranging from LD 50 = 7.3 × 10 5 to 4.9 × 10 9 conidia mL -1 and LT 50 = 7.74 to 9.97 d at 10 8 conidia mL -1 . Significant differences (P ≤ 0.05) were observed on longevity at 10 6 conidia mL -1 . Longevity was 29% less compared to the control. This result on longevity at relatively low dose is relevant due to the fact that decrease possibilities to find live quarantine insects at the moment of harvest. Fertility, fecundity and egg size showed no differences (P > 0.05).

seudococcus viburni Signoret (Hemiptera: Pseudococcidae), commonly named obscure mealybug, is a cosmopolitan insect present in temperate climates areas of the Northern Hemisphere, South Africa, Australia, New Zealand, and Chile (González et al., 2001). It is polyphagous, attacking ornamentals, fruit trees, forest trees, and cactus. It has an oval and wax covered body. The direct damage is produced by suction of vascular juices and the production of sweet honeydew (González, 1989). The affected parts of the plant present a dark oily aspect, due to the sweet honeydew that is infected by fungus and dust, commonly named sooty mould (Artigas, 1994), which greatly depreciates the quality of fruit such as table grapes (Vitis vinifera L.) (Bournier, 1976). Obscure mealybug is a quarantine insect of mandatory control that hides in places of difficult access (González, 2003b). Most Pseudococcus species cause low direct damage and are considered minor plagues, and major plagues for being quarantine insects on grapes, pomaceous, and stone fruit (González, 2003a). The markets with quarantine restriction to P. viburni are Brazil, Mexico, Japan, and Korea (González and Volosky, 2006). Within insect pathogens, entomopathogenic fungi are the main contenders for commercial production and use against homopterous pest insects (Lacey et al., 2001). Entomopathogenic fungi do not cause immediate mortality. The activity of infected hosts can be altered just before death due to microbial infections (Hajek et al., 2008).

A minimal amount of pathogen exists to start a disease (Alves and Lecuona, 1998), as well as a positive correlation between infectious conidia and mortality by mycosis (Ferron, 1978). There are sublethal effects that can affect the reproductive output of infected adult females with entomopathogenic fungi before death . On the other hand, this infection does not have always the same effect on reproduction, being able to present a decrease or increase on fecundity, as well as a decrease on longevity (Hajek et al., 2008). Whereas the majority of studies assessing biocontrol agents deal with their ability to produce mortality in the target pest, a considerable number have also being assessed for the impact that infection may produce on host behaviors. These include effects on developmental time, fecundity and feeding (Blanford and Thomas, 2001). Different studies with entomopathogenic fungi have shown effects over the reproductive potential of infected females. Castillo et al. (2000) show that Metarhizium anisopliae affect females fecundity of Ceratitis capitata Wiedemann, as well as Blatella germanica (Linneo) (Quesada-Moraga et al., 2004) and Anopheles gambiae (Giles) (Scholte et al., 2006), among other species.

The aim of this research was to evaluate the virulence of different doses of M. anisopliae var. anisopliae isolate Qu-M984 on developmental stages of P. viburni and the pre-lethal effects fertility, egg size, fecundity, and longevity on the adult female.

MATERIALS AND METHODS

Insects

Pseudococcus viburni Signoret (Hemiptera: Pseudococcidae) were obtained from an artificial mass rearing unit maintained since 2005 by Instituto de Investigaciones Agropecuarias INIA Quilamapu (Chillán, Chile). Insects were reared on potato (Solanum tuberosum L.) buds and held in total darkness at 23 ± 1 ºC and 80% ± R.H. The four developmental stages were used for experimentation and the selection criteria was the movement capacity and acceptable healthy. The three nymph stages and adult females were identified under stereomicroscope of 40X, according to insect size and presence of marginal filaments, using the classification of the four developmental stages of P. viburni females described by Oyarzún (2004).

Conidial production

Metarhizium anisopliae var. anisopliae isolate Qu-M984, was obtained from the entomopathogenic fungi collection in INIA Quilamapu (Chillán, Chile). The fungus was cultivated on potato dextrose agar (PDA) medium in Petri dishes, inoculated in biological safety cabinet and placed in an incubator at 25 ºC for 2 wk in complete darkness until sporulation. Conidia were harvested directly by scraping from the surface culture and suspended in sterile distilled water containing 0.01% Tween 80 (Sigma®, Steinheim, Germany) to produce a homogenous suspension. The concentration of the suspension was determined by using a haemocytometer (BOECO, Neubauer, Germany) under microscope 1000X, and diluted with sterile distilled water plus 0.01% Tween 80 to obtain the conidial concentrations for each trial.

Virulence

Insects of the four developmental stages were placed on cylindrical flasks of 39 mL and sprayed with the doses 10 5 , 10 6 , 10 7 and 10 8 conidia mL -1 , the control was treated with sterile distilled water. All treatments contained 0.01% Tween 80. A Potter spray tower at 0.1 MPa was used to apply the conidia over the individuals that were immediately transferred to a piece of potato buds serving as food and maintained at 23 ± 1 ºC in the dark. Mortality was recorded for each individual every 2 d until 19 d post application. All dead insects were placed individually in a humid chamber at 25º C to facilitate the appropriate sporulation of M. anisopliae on the insect surface to confirm mycosis (Butt and Goettel, 2000). The criteria to determine mortality was the response to mechanical stimuli, being considered dead insects those with no evident reaction.

The experimental design was completely randomized, with five treatments and four replicates, each experimental unit consisting of 10 insects, for each of the four developmental stages of the female. Mortality rates were separated by Tukey test (P < 0.05) and compared by oneway ANOVA using the statistical program SAS (SAS Institute, 2003). The recorded means of mortality were corrected using Abbott formula (Abbott, 1925). Lethal dose (LD 50 ) and lethal time (LT 50 ) were estimated using Probit analysis (Statgraphics, 2000). Fit of the regression lines were verifying using χ 2 test (SAS Institute, 2003).

Pre-lethal effects

Fertility and egg size. The doses used on the trials of fertility, egg size, longevity, and fecundity were 10 5 and 10 6 conidia mL -1 . Control individuals were treated with sterile distilled water. All treatments contained Tween 80 at 0.01%. The low doses 10 5 and 10 6 conidia mL -1 were selected to increase possibilities of a slow infection by the fungus in the insect and analize sublethal effects before death, avoiding a high and quick mortality. Insects selected were adult females. Egg fertility was estimated registering the eclosion of eggs by number of nymphs emerged, checking the total eclosed and non eclosed eggs as well as the empty chorion left by eclosed eggs. The recorded means were analyzed using Chi-square test with an experimental unit of 25 eggs per female (SAS Institute, 2003). Egg size was recorded by taking samples of 25 eggs for each female, separating the wax and placing them in horizontal position, in order to record the length by digital pictures, using UTHSCSA ImageTool program (University of Texas Health Science Center, 1996). The recorded means of egg size were separated by Tukey test (P < 0.05) and compared by one-way ANOVA using the statistical program SAS (SAS Institute, 2003).

Fecundity and longevity. The treatments used on theses trials were the same than in fertility and egg size. Insects selected were adult females; the experimental unit consisted of 10 insects, replicated four times. The insects had contact with active males in the artificial rearing, prior to selection and, before fungi spraying, were added three males for each 10 females for 24 h to increase fecundity possibilities. According to Oyarzún (2004) one male is capable to fecundate six females daily. All individuals were sprayed with M. anisopliae Qu-M984 at the doses mentioned above using a Potter spray tower. Before spraying the insects, they were placed in cylindrical flasks of 39 mL in groups of 10, later each insect was located separately in flasks of the same characteristics and fed with potato buds held at 23 ± 1 ºC in dark and 80% ± R.H. Oviposition and mortality were individually recorded under stereomicroscope of 40X, every 48 h, until death of all insects. Fecundity of each female was determined counting number of eggs every 48 h. To assess longevity mortality was recorded, considering dead insects those with no response to mechanical stimuli. The recorded means of fecundity and longevity were separated by Tukey test (P < 0.05) and compared by one-way ANOVA using the statistical program SAS (SAS Institute, 2003).

RESULTS AND DISCUSSION

Virulence

The isolate studied demonstrated pathogenicity for all developmental stages when clumped together (P ≤ 0.05), being mortality dose-dependent, mean values varying from 48% (10 5 conidia mL -1 ) to 78% (10 8 conidia mL -1 ), compared to 25.6% in the control ( Figure  1). There were no differences among developmental stages (P > 0.05) with the only exception when using the dose 10 7 conidia mL -1 (P < 0.05) Likewise, there were differences (P < 0.05) within doses for nymph II, nymph III, and adult developmental stages, although no difference was found for nymph I (P > 0.05) ( Table 1).

Lethal time values at the higher dose (10 8 conidia mL -1 ) ranged from 7.74 to 10 d among the different developmental stages (Table 2). All regression lines were verified and showed homogeneity for goodness of fit by χ 2 . LT 50 obtained demonstrated that the dose 10 8 conidia mL -1 decreased life time period in all developmental stages, affecting the reproductive potential of females prior to death, decreasing fertile period and oviposition time. There is no evidence of TL 50 with entomopathogenic fungi and P. viburni, but authors have documented that Trialeurodes vaporariorum and Bemisia tabaci, treated with B. bassiana obtained TL 50 ranging from 5.9 to 7.4 d with 10 7 conidia mL -1 on fourth stage (Quesada-Moraga et al., 2006). Therefore, the highest dose used (10 8 conidia mL -1 ) needed a short time to reach 50% mortality on all developmental stages of P. viburni, with similar values obtained by entomopathogenic fungi in insects of the same kind.

Pre-lethal effects

Fertility and egg size. No significant differences were found (P > 0.05) ( Table 3) in egg fertility and egg size. In fertility was recorded 94%, 95% and 93% of eclosed eggs (control, 10 5 and 10 6 conidia mL -1 , respectively). The eggs of infected insects by entomopathogens can result in total or partial infertility, or contain infected embryos that will be inoculum for the dispersion of the pathogen (Alves and Pereira, 1998). In this study effects of decreasing egg fertility on survival females at doses of 10 5 and 10 6 conidia mL -1 were not detected. Similar results on egg fertility were observed on B. tabaci (Hemiptera: Aleyrodidae) and Leptinotarsa decemlineata (Coleoptera: Chrysomelidae) using B. bassiana (Fargues et al., 1991). Likewise, inoculating directly on eggs of Bemisia argentifolii with Verticillium lecanii Zimmermann, this showed no pathogenic effects, however all other developmental stages of the species were affected by the pathogen (Gindin et al., 2000   in the case of Megalurothrips sjostedti at concentrations of 10 6 to 10 8 conidia mL -1 (Ekesi and Maniania, 2000) and Cylas puncticollis at concentrations from 1 × 10 6 to 3.0 × 10 7 conidia mL -1 (Ondiaka et al., 2008).

Fecundity. Oviposition throughout the time reached a peak on the fourth day, and then decreased gradually, giving the same response in all treatments. No significant difference was found (P > 0.05) (CV = 77%) ( Figure  2). The total laid eggs counted throughout the lifetime of each adult female was 177 eggs to control, 134 eggs with 10 6 and 120 with 10 5 conidia mL -1 . Likewise, in the number of eggs d -1 by female ranged among 9.1 and 13.4 eggs and no significant differences were found (P > 0.05, CV = 83%) ( Table 3).

There was no effect of M. anisopliae isolate Qu-M984 on fecundity of P. viburni in the assessed doses. This could be due to sample heterogeneity, evidenced by the high coefficient of variation (CV= 77%) not being possible to detect statistical differences. The distribution of the number of eggs laid throughout the female life span does not change among treatments when exposed to M. anisopliae (Figure 2), which differs to other studies where insects exposed to entomopathogenic fungi have shown higher initial number of eggs than the control, however, without statistical differences (Arthurs and Thomas, 2000;Pires et al., 2008). The development of a disease produced by a pathogen could be affected by biotic factors on the host, such as reproductive properties characteristics of the insect, population characteristics (individual susceptibility), and insect habits, among others (Alves and Lecuona, 1998). On the other hand, it should be considered the number of the individuals of the population inoculated, since the higher densities of insects increase the contact possibilities among them and consequently horizontal transmission, something that was not observed in this study, because the insects under study were isolated individuals. Also, the fitness of a specimen is directly dependent on the number of viable offspring produced, and both the pathogen and the host adopt strategies to maximize reproductive output (Baverstock et al., 2006;Roy et al., 2006). Also temperature can influence fecundity response. Leptinotarsa decemlineata treated with B. bassiana decreased its reproductive output at 23 ºC, but not at 25 ºC (Fargues et al., 1991). In our research this effect was not evaluated, since a constant 23 ± 1 °C was used.

Any measurable behavior presents response thresholds and the form or magnitude often increases with dose (Hoy et al., 1998), therefore, it is possible that when doses are increased the reproductive output decreases. There are no registers of the effects of entomopathogens on P. viburni, however, it is possible to make comparisons with other insects. Similar results to this study were observed in the reproductive output of Tutta absoluta (Pires et al., 2008), using doses of 10 7 conidia mL -1 of M. anisopliae and Diuraphis noxia (Wang and Knudsen, 1993) using B. bassiana. Other studies have shown positive results on this topic, for example Cylas puncticollis (Ondiaka et al., 2008), Anoplophora glabripennis (Hajek et al., 2008), Megalurothrips sjostedti (Ekesi and Maniania, 2000), among others.

Longevity.

A significant difference was found in longevity of adult females among treatments (P ≤ 0.05) (Table 4, Figure 3). At 10 6 conidia mL -1 there were differences compared to the control, however, no differences were detected between 10 5 conidia mL -1 and control. With the dose 10 6 conidia mL -1 was recorded a decrease of 11.8 d compared to the control (29%) (CV = 62%; P ≤ 0.05). The oviposition time was not significantly (P > 0.05) affected by the fungus, varying among 12 to 14.7 d ( Table 4). Longevity of adult females was affected by M. anisopliae isolate Qu-M984 at 10 6 conidia mL -1 . This could be the threshold concentration when the fungi start to affect longevity in females of the obscure mealybug. The decreased longevity at relatively low dose (   conidia mL -1 ), reduces the direct damage caused by the insect, which does not have economic importance, but takes relevance when decreasing possibilities to find live quarantine insects at the moment of harvest. Similar results to this study were obtained with Diuraphis noxia Kurdiumov, when pre-lethal effects of entomopathogenic fungi were evaluated and found effects only on longevity (Wang and Knudsen, 1993).

CONCLUSIONS

Metarhizium anisopliae var. anisopliae isolate Qu-M984 demonstrated pathogenicity for Pseudococcus viburni, under controlled laboratory conditions. Virulence was dose-dependent and showed high mortality in short term. Low doses of the isolate do not affect pre-lethal reproductive effects, such as fecundity, fertility and egg size, however, longevity is affected to a low dose. These results showed that M. anisopliae isolate Qu-M984 is an option to control this pest; nevertheless research at field conditions is needed.

ACKNOWLEDGEMENTS

We would like to thank Maritza Tapia and Lionel Finot for critical review of the manuscript. And we would also like to thank Ricardo Ceballos and Marisol Berti for assistance with the statistical analysis. This study was supported by INIA Quilamapu, Chillán, Chile, and INNOVA Chile.

LITERATURE CITED

Figure 1 .
1Mortality rate (± SE) 19 d after aplication of Metarhizium anisopliae (conidia mL -1 ) for all developmental stages of Pseudococcus viburni.

Figure 2 .
2Mean eggs laid by adult females of Pseudococcus viburni exposed to Metarhizium anisopliae isolate Qu-M984.

Figure 3 .
3Longevity of adult females of Pseudococcus viburni treated withMetarhizium anisopliae isolate QU-M984.

). On the other hand, other studies have shown effects on egg fertility of survival females infected by M. anisopliae, asTable 1. Virulence of Metarhizium anisopliae isolate QU-M984 on the different developmental stages of Pseudococcus viburni. (± SE) within a column followed by the same lower case letter and within row followed by the same upper case letter are not significantly different according to Tukey test (P = 0.05).Nymph I
37.5 ± 4.8aA
50.0 ± 10.8aA
62.5 ± 21.7aA
52.5 ± 11.1abA
65.0 ± 8.7aA
Nymph II
25.0 ± 5.0aA
52.5 ± 2.5aABC
70.0 ± 12.2aBC
42.5 ± 2.5aAB
80.0 ± 10.8aC
Nymph III
27.5 ± 11.1aA
40.0 ± 5.8aAB
52.5 ± 12.5aABC
85.0 ± 6.5cC
80.0 ± 8.2aBC
Adult
12.5 ± 4.8aA
50.0 ± 12.9aAB
50.0 ± 10.8aAB
80.0 ± 7.1bcB
87.5 ± 6.3aB

Means Dose (conidia mL -1 )

Control
10 5
10 6
10 7
10 8
Development stage

conidia mL -1
d
Nymph I
4.9 × 10 9 ± 3.93NS
10.0 ± 1.02NS
Nymph II
7.3 × 10 6 ± 3.75
7.74 ± 1.10
Nymph III
4.7 × 10 6 ± 4.19
9.72 ± 1.18
Adult
7.3 × 10 5 ± 4.02
8.61 ± 1.36

Table 2 .
2LD 50 and LT 50 of Metarhizium anisopliae isolate QU-M984 on the
different developmental stages of Pseudococcus viburni.

Development stage
LD 50
LT 50

LD 50 at 19 d post-inoculation; LT 50 at 10 8 conidia mL-1; NS: non significant (P = 0.05); ±
Standard error.

SE: standard error. Means within columns followed by the same letters are not significantly different according to Tukey test (P = 0.05).10 6

conidia mL -1
d
10 6
29.62 ± 2.25a
12.06 ± 1.29a
10 5
35.68 ± 2.30ab
12.18 ± 1.03a
Control
41.43 ± 1.36b
14.74 ± 1.11a

Table 4. Average (± SE) longevity and oviposition time for Pseudococcus
viburni treated with Metarhizium anisopliae isolate QU-M984.

Dose
Longevity rate
Oviposition time

d: days after treatment,

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