Table 5.

Behaviour-related results of interventional studies

ID	Author	Behaviour-related findings
39	Rambaud-Althaus et al 32	‘The proportion of children appropriately managed (antimalarials, antibiotics, zinc, and rehydration prescribed when needed only) was similar in the two intervention arms: 62 % in paper (range: 55% – 74 %, n = 171 ); and 63 % in the electronic arm (range: 52% – 72 %, n = 167 ). The proportion of children appropriately managed was significantly lower ( p < 0.001 ) in the control arm (37%, range: 29% – 44 %, n = 166 ) than in the paper ( RR = 1.7 [ 1.3– 2.2 ] ) and electronic arm ( 1.7 [1.3 – 2.2 ] ).’
63	Shao et al 33	‘Health workers pointed out that the ALMANACH assisted them to reduce antibiotic and antimalarial prescription as the device walked them step-by-step through the consultation starting from diagnosis to treatment including calculation of proper dosage of required drugs. Thus, the majority of the study participants (10 smartphone/11 tablet) stated that both devices reduced antibiotic prescription compared to routine practice. “Yes, before I was prescribing antibiotics as antibiotics, I was just prescribing antibiotics, but truly now you don’t believe, now I know many diseases are febrile diseases, they don’t need antibiotics ”. ’ (IDI, female, smartphone, very high uptake) ‘More than half of the respondents (8 smartphone/7 tablet) highlighted that the ALMANACH enabled correct treatment. “There are many advantages; first, the phone is a reference point in the sense that if you have forgotten what the patient is suffering from, or treatment or medication, by following the instructions in the phone you will know the diagnosis and medicine to that diagnosis. So the phone helps a lot ”. ’ (IDI, male, smartphone, very low uptake)
120	Palazuelos et al 46	‘Use of the mHealth tool generally resulted in more accurate answers when compared to the paper-based tool. For 6 of 7 practice test questions, the mean score among those who answered with the mHealth tool was notably higher than the mean score among respondents who answered with the paper-based tool. In general, the difference was greatest in the questions that asked for pediatric doses based on age and weight, as opposed to standardized doses and courses for adults. Although not coded nor quantified, the majority of the errors with each tool followed a few general themes. For the paper-based tool, the community health workers often found it challenging to find the 3 different dosing elements needed (dose, schedule, and duration) as they were often in disparate locations without any clear pattern to follow. For the mHealth tool, the community health workers produced a wrong result if they inadvertently entered information incorrectly at some stage of the algorithm ( ie, if they entered in a wrong gender, age, weight, etc).’ ‘Overall, the CHWs in both countries accepted the mHealth tool as a satisfactory tool that was appropriate for use in dosing a medicine. Some CHWs noted that using the mHealth tool on a phone would be a way to gain credibility in the community. The people, upon seeing us look in the book, think badly of us. With the phone, they think we are important. The phone is a more acceptable way to access information in front of the patient so as to not lose face. ’
2	Abouda et al 37	‘The number of drugs prescribed per patient who received drug prescription decreased by 18.8 % in the impact survey (3.2 vs 2.6, p < 0.001 ).’
125	Segal et al 35	‘Dosing accuracy improved from 64.7 % (among 156 prescriptions) to 92.4 % (among 210 prescriptions) when providers used the app. Dosages prescribed after implementation were 40 % more likely to be correct (relative risk: 1.39; 95%  CI 1.16 to 1.68; p = 0.0005 ). All providers appeared to dose medications more accurately after the intervention. ’
87	Adams et al 38	‘ The study nurse correctly identified all algorithm-indicated antidepressant recommendations ( n = 74, 100%) and communicated all to the study clinical officer. ’
47	Praveen et al 47	‘Among those not on medications, 31 % (11/36) were recommended for treatment by the decision support tool. The physician commenced all these patients ( n = 11 ) on BP-lowering treatment. ’
121	Catalani et al 36	‘Although providers rated the messages relatively highly, they found the accuracy and actionability of the clinical decision support system problematic. Providers indicated that roughly over a quarter of the reminders were not correct for that particular patient and that particular day. Moreover, slightly less than half of the reminders were not considered actionable on that day. ’
124	Bessat et al 34	‘Positive effects were mentioned to be better management of children (5 IDI, 1FGD), facilitation in treatment decision-making and dosage calculation (7IDI, 1FGD), standardization of treatment (2IDI) and rational use of medicines (6IDI). The application guides the clinician trough the assessment of the child up to the treatment and the counselling part. At the end, a free text question gives room to the clinician to add additional classifications and treatments. Half of the study participants reported not to add an antibiotic when the application did not recommend it, and mentioned it helped them to rationalize the use of drugs. However, the other half of the participants (6IDI, 1FGD) admitted to sometimes add an antibiotic even though the application did not recommend it. Reasons mentioned were: to calm or treat cough (5IDI), to prevent re-consultation (2IDI, 1FGD), to cover severe diseases or prevent worsening of the disease (3IDI) and in cases of fever with a negative malaria RDT result (1IDI, 1FGD ).’

ALMANACH, Algorithm for Management of Childhood Illness; BP, blood pressure; CHW, community health worker; FGD, focus group discussion; IDI, in-depth interview; RDT, rapid diagnostic test; RR, relative risk.